The Paradigm Shift and Normative Pathways of Marxist Research in the Age of Digital Intelligence

General Secretary Xi Jinping has profoundly pointed out: “History shows that an era of great social transformation must be an era of great development in philosophy and social sciences.” [1]1 Xi Jinping, “Speech at the Symposium on Philosophy and Social Sciences Work” (May 17, 2016). The “Proposal of the Central Committee of the Communist Party of China on Formulating the 15th Five-Year Plan for National Economic and Social Development” [2]2 The “15th Five-Year Plan” refers to the period 2026-2030. proposes to lead the transformation of research paradigms with artificial intelligence. The year 2026 is not only the first year of the “15th Five-Year Plan” but also the tenth anniversary of General Secretary Xi Jinping’s important “5·17” speech. [3]3 The “5·17” speech refers to Xi Jinping’s 2016 address on philosophy and social sciences. Standing at this historical juncture, the cause of philosophy and social sciences faces new development opportunities and shoulders even greater responsibilities of the times. Marxism is an open theoretical system that continuously develops with the progress of the times, practice, and science; its vitality lies in constantly responding to the questions of the era and absorbing the essence of civilization. Facing a new round of technological revolution and industrial transformation, how can the scientific nature and revolutionary character of Marxist theoretical research be maintained amidst technological empowerment? How can the seriousness of academic norms be upheld when innovating research paradigms? These are major issues that urgently need to be addressed in advancing Marxist research during the “15th Five-Year Plan” period.

习近平总书记深刻指出：“历史表明，社会大变革的时代，一定是哲学社会科学大发展的时代。”《中共中央关于制定国民经济和社会发展第十五个五年规划的建议》提出，以人工智能引领科研范式变革。2026年既是“十五五”开局之年，也是习近平总书记“5·17”重要讲话发表十周年。站在这一历史节点上，哲学社会科学事业迎来新的发展机遇，也肩负着更加重大的时代责任。马克思主义是随着时代、实践、科学发展而不断发展的开放的理论体系，其生命力在于不断回应时代之问、汲取文明精华。面对新一轮科技革命和产业变革，如何在技术赋能中保持马克思主义理论研究的科学性与革命性？如何在创新研究范式时坚守学术规范的严肃性？这些都是“十五五”时期推进马克思主义研究亟须回答的重大课题。

I. The Era Opportunity of Digital Intelligence Technology Empowering Theoretical Research

一、数智技术赋能理论研究的时代机遇

“The distinction between various economic epochs does not lie in what is produced, but in how it is produced, and by what means of labor it is produced.” [4]4 Karl Marx, *Capital*, Volume 1, Chapter 7, Section 1. As a new type of means of labor, digital intelligence technology is profoundly changing the academic production mode of Marxist theoretical research.

“各种经济时代的区别，不在于生产什么，而在于怎样生产，用什么劳动资料生产。”数智技术作为一种新型劳动资料，正深刻改变马克思主义理论研究的学术生产方式。

Text mining technology broadens the research horizon of classical texts. Indeed, classical literature is the source and living water of Marxist theory. However, traditional methods of literature research are limited by the processing capacity of individual researchers, making it difficult to grasp the evolutionary process of the ideological system as a whole. With the help of digital intelligence technologies such as natural language processing, text mining, and semantic retrieval, researchers can achieve retrieval, comparison, and correlation analysis of classical texts on a larger scale. For example, using the “Jingdian Semantic” retrieval function of the CPC Ideological and Theoretical Resources Database, [5]5 A comprehensive digital database of CPC theoretical texts and Marxist classics. inputting “machinery is not capital nor will it lose its use value” with a 60% similarity threshold can link to the original exposition on page 777 of Volume 2 of the *Selected Works of Marx and Engels*: “When the system of machinery is no longer capital, it will not lose its use value.”

文本挖掘技术拓宽经典文本的研究视野。诚然，经典文献是马克思主义理论的源头活水。但传统的文献研究方法受限于研究者个体的处理能力，难以从整体上把握思想体系的演进过程。借助自然语言处理、文本挖掘和语义检索等数智技术，研究者能够在更大范围内实现经典文本的检索、比对与关联分析。例如，借助中国共产党思想理论资源数据库“金典语义”检索功能，输入“机器不是资本也不会失去其使用价值”以60%相似度检索，即可关联至《马克思恩格斯选集》第2卷第777页关于“机器体系不再是资本时，它也不会失去自己的使用价值”的原始论述。

Based on this technology, researchers can not only precisely locate the original source through semantic correlation but also obtain the version information, publication date, and contextual background of the cited text on the same interface, greatly improving the efficiency and accuracy of textual research.

基于该技术，研究者不仅能以语义关联精准定位原始出处，更可在同一界面获取引文的版本信息、出版年代及上下文语境，极大地提升了文本研究的效率与精度。

Computational social science provides new means for theoretical testing. In *Capital*, Marx revealed the law of the tendency of the general rate of profit to fall. However, past research has found it difficult to test this theoretical logic in the real world. Computational social science in the age of digital intelligence provides a digitalized, virtualized experimental field for theoretical testing. For example, by introducing multi-agent simulation modeling technology, researchers can set up capital agents with differentiated characteristics in a virtual space, set initial conditions according to the profit rate formula P'=m/(c+v), and dynamically simulate the evolutionary trajectory of the general rate of profit under conditions where the rate of surplus value is relatively stable and the organic composition of capital gradually increases. This technical means has advantages such as repeatability, adjustability, and low cost. It can simulate the generation conditions, operational logic, and evolutionary process of theoretical propositions in a virtual space. This not only helps deepen the understanding of important theses by classical writers but also provides methodological support for grasping the new changes and new characteristics of contemporary capitalism.

计算社会科学为理论检验提供新的手段。马克思在《资本论》中揭示了一般利润率趋向下降的规律。但过往研究难以将这一理论逻辑置于现实世界中检验。数智时代的计算社会科学为理论检验提供了数字化、虚拟化的实验场域。例如，通过引入多主体仿真建模技术，研究者可在虚拟空间中设定具有差异化特征的资本主体，依据利润率公式P'=m/（c+v）设定初始条件，在剩余价值率相对稳定、资本有机构成逐步提高的条件下，动态模拟一般利润率的演化轨迹。这一技术手段具有可重复、可调适、低成本等优势，能够在虚拟空间中模拟理论命题的生成条件、运行逻辑和演化过程，既有助于深化对经典作家重要论断的理解，也可为把握当代资本主义的新变化与新特征提供方法支撑。

Generative artificial intelligence improves the efficiency of knowledge production. The widespread application of generative AI marks the entry of knowledge production into a new stage of “human-machine collaboration.” AI can not only improve the efficiency of researchers in retrieving literature and summarizing reviews but also help researchers stimulate innovative ideas through continuous dialogue with AI. More importantly, AI can help researchers break down language barriers and disciplinary boundaries, allowing them to focus their energy on refining core viewpoints, truly “using good steel on the blade.” [6]6 A Chinese idiom meaning to use resources where they are most effective.

生成式人工智能提高知识生产效率。生成式人工智能的广泛应用，标志着知识生产进入“人机协同”的新阶段。人工智能不仅能够提高研究者检索文献与综述提炼的效率，也能够让研究者在与人工智能的不断对话中激发创新点。更重要的是，人工智能可以帮助研究者打破语言障碍与学科壁垒，将精力聚焦于核心观点的凝练，真正做到“好钢用在刀刃上”。

However, the advancement of technological tools is not directly equivalent to the scientific nature of theoretical output. While embracing the dividends of digital intelligence technology, one must still be vigilant about the risks that digital intelligence technology may bring.

但技术工具的先进性并不直接等同于理论产出的科学性。在拥抱数智技术红利之时，仍须警惕数智技术可能带来的风险。

II. Multiple Academic Risks Arising from the Usurpation of Technological Rationality

二、技术理性僭越带来的多重学术风险

General Secretary Xi Jinping has profoundly pointed out: “Artificial intelligence brings unprecedented development opportunities, but also unprecedented risks and challenges.” [7]7 Xi Jinping, “Speech at the 2024 World Internet Conference Wuzhen Summit” (November 20, 2024). For Marxist research, the usurpation of technological rationality has sounded an alarm in three dimensions: epistemology, ideology, and subjectivity.

习近平总书记深刻指出：“人工智能带来前所未有发展机遇，也带来前所未遇风险挑战。”对于马克思主义研究而言，技术理性的僭越已在认识论、意识形态与主体性三个维度敲响警钟。

The first is epistemological risk. Marx clarified that the essence of dialectics is “in its positive understanding of what exists, it simultaneously includes a negative understanding of what exists.” [8]8 Karl Marx, *Capital*, Volume 1, Afterword to the Second German Edition. When discussing machines imitating the complex computational behavior of humans, the British mathematician and logician Alan M. Turing noted: “If one wants to make a machine imitate the behavior of a human computer in a certain complex operation, one must ask how the human does it, and then translate the answer into a form of instruction table.” [9]9 Alan Turing, “Computing Machinery and Intelligence” (1950). This means that the operation of machine intelligence is essentially the transformation of complex behavior into executable programs. Digital intelligence technology follows the formal logic of “A=A,” pursuing certainty and linear causality. However, the essence of materialist dialectics emphasizes the dialectical movement of contradiction where “A is both A and non-A.” If researchers over-rely on algorithmic models in their studies, they will often unconsciously use static mathematical functions to analyze social historical processes full of contradictory tensions, thereby neglecting the laws of quantitative and qualitative change and the negation of the negation that drive social development. Furthermore, digital intelligence technology often disassembles the complex social organism into isolated, calculable individual data points. This fragmented research severs the internal connection between data indicators and relations of production, class structure, and historical background, blocking the Marxist thinking path from the abstract to the concrete, trapping research in the epistemological dilemma of “seeing the trees of data examples but not the forest of historical materialism.” [10]10 A play on the Chinese idiom “not seeing the forest for the trees.”

一是认识论风险。马克思阐明辩证法实质上是“在对现存事物的肯定的理解中同时包含对现存事物的否定的理解”。英国数学家、逻辑学家艾伦·图灵（Alan M. Turing）在讨论机器模仿人的复杂运算行为时指出：“如果要使一台机器模仿人类计算机在某种复杂运算中的行为，必须问一问人是怎样做到这一点的，然后把答案翻译成指令表形式。”这意味着，机器智能的运行，实质上是将复杂行为转化为可执行的程序。数智技术遵循“A=A”的形式逻辑，追求确定性与线性因果。然而，唯物辩证法的本质是强调“A既是A又是非A”的辩证矛盾运动。在研究中若过度依赖算法模型，研究者往往会不自觉地用静止的数学函数去分析充满矛盾张力的社会历史过程，从而忽视推动社会发展的量变质变与否定之否定规律。此外，数智技术往往将复杂的社会机体拆解为孤立的、可计算的单个数据点。这种碎片化研究割裂了数据指标与生产关系、阶级结构、历史背景的内在联系，阻断马克思主义从抽象到具体的思维路径，使研究陷入“只见数据例证之树木，不见历史唯物主义之森林”的认识论困境。

The second is ideological risk. “It is not the consciousness of men that determines their being, but, on the contrary, their social being that determines their consciousness.” [11]11 Karl Marx, *A Contribution to the Critique of Political Economy*, Preface (1859). Digital intelligence technology is not entirely value-neutral; its application in academic research often carries ideological risks. In December 2022, the U.S. independent investigative website “The Intercept” disclosed that a subordinate agency of the U.S. Department of Defense had manipulated topics and used deceptive propaganda on the social media platform Twitter (now X) to intervene in the public opinion perception of people in the Middle East, continuously promoting narratives favorable to the United States and its allies. [12]12 “The Intercept,” December 2022, report on U.S. military information operations. Taking the recently widely discussed open-source overseas AI agent as an example, this type of tool possesses strong autonomous decision-making and system coordination capabilities. If introduced into the academic research process without discrimination, it may amplify risks such as technological dependence, data distortion, and value bias. Over-relying on overseas tools with opaque underlying logic, training corpora, and platform rules for key research steps such as literature retrieval, data scraping, and even viewpoint extraction is tantamount to ceding the power of academic screening to their built-in filtering mechanisms. This machine-agent academic behavior can easily marginalize Marxist positions and viewpoints in research, continuously “feeding” researchers with corpora containing specific political tendencies. Blindly relying on such tools preset with specific ideologies will not only cause researchers to gradually lose their political discernment but also directly threaten the security of the national ideological and academic fields. Additionally, algorithm-based recommendation mechanisms that rely on user habits can easily form closed information cocoons. Long-term reliance on this method of acquiring academic resources will narrow researchers’ horizons and rigidify their thinking.

二是意识形态风险。“不是人们的意识决定人们的存在，相反，是人们的社会存在决定人们的意识。”数智技术并非完全的价值中立，其在学术研究中的应用往往伴随着意识形态风险。2022年12月，美国独立调查网站“拦截”披露：美国国防部下属机构曾在社交媒体推特（现为X）通过操控话题、欺骗性宣传等手段干预中东民众舆论认知，持续推进有利于美国及其盟友的叙事。以近期引发广泛关注的境外开源AI智能体为例，这类工具具备较强的自主决策和系统调配能力，若不加甄别地引入学术研究过程，就可能放大技术依赖、数据失真和价值偏向等风险。将文献检索、数据抓取乃至观点提炼等关键研究环节过度交由底层逻辑、训练语料与平台规则并不透明的境外工具处理，无异于将学术筛选权拱手让渡于其内置的过滤机制。这种机器代理的学术行为，极易在研究中边缘化马克思主义的立场与观点，向研究者持续“投喂”带有特定政治倾向的语料。若盲目依赖这类预设了特定意识形态的工具，不仅会导致研究者逐渐丧失政治鉴别力，更直接威胁到国家意识形态领域与学术领域的安全。此外，基于用户习惯的算法推荐机制容易形成封闭的信息茧房。长期依赖这种方式获取学术资源，会使研究者视野窄化、思维固化。

Third, the risk of subjectivity. Academic research is, in essence, a creative spiritual activity of human beings. With the proliferation of AI writing tools, the phenomenon of “thought outsourcing” has begun to spread, as some researchers attempt to use machine computing power to replace humanistic concern and dialectical thinking. If this continues over the long term, it will inevitably lead to the degradation of researchers’ capacity for independent thought and theoretical innovation, reducing them to appendages of technology. More importantly, the underlying logic of generative artificial intelligence is probabilistic generation based on existing corpora, and the veracity of its output requires careful discernment. The online literature already published is of mixed quality, and AI is highly prone to generating “machine hallucinations” — fabricated facts and invented references — during the generation process. If such content is cited without verification, it will not only trample on the academic truth-seeking baseline of “every statement must have evidence,” but will fundamentally undermine the scientificity and normativity of Marxist theory. Recognizing risks is not about shrinking back; only by knowing where to stop can one proceed steadily. Only by clearly delineating the boundaries of technological application can we truly master and correctly use the tools.

三是主体性风险。学术研究本质上是人的创造性精神活动。随着人工智能写作工具的普及，“思维外包”现象开始蔓延，部分研究者试图用机器算力替代人文关怀与辩证思维。长此以往，必然导致研究者独立思考与理论创新能力的退化，沦为技术的附庸。更重要的是，生成式人工智能的底层逻辑是基于已有语料进行概率性生成，其输出内容的真实性需要仔细鉴别。现已发布的网络文献良莠不齐，人工智能在生成过程中极易产生虚构事实、捏造文献的“机器幻觉”。如果不加核实地引用，不仅会践踏“言必有据”的学术求真底线，更将从根本上动摇马克思主义理论的科学性与规范性。识险不是为了却步，知止方能行稳。唯有清晰划定技术应用的边界，才能真正驾驭和正确使用工具。

III. Establishing Norms for Technological Application Guided by Marxism

三、确立马克思主义指导的技术应用规范

General Secretary Xi Jinping emphasized during the 20th collective study session of the Political Bureau of the CPC Central Committee that “we must grasp the trends and laws of AI development, accelerate the formulation and improvement of relevant laws, regulations, policies, application norms, and ethical guidelines, and build systems for technological monitoring, risk early warning, and emergency response to ensure that AI is safe, reliable, and controllable.” This important exposition provides the fundamental guidance for us to address technological challenges and establish academic norms in the age of digital intelligence.

习近平总书记在中共中央政治局第二十次集体学习时强调，“要把握人工智能发展趋势和规律，加紧制定完善相关法律法规、政策制度、应用规范、伦理准则，构建技术监测、风险预警、应急响应体系，确保人工智能安全、可靠、可控”。这一重要论述，为我们在数智时代应对技术挑战、确立学术规范提供了根本遵循。

Uphold the guiding position of Marxism in technological application. When the roots are firm, the blossoms and fruits will flourish. Marxism is the fundamental guiding ideology for our Party’s founding and governance of the country, and for rejuvenating the Party and the nation. To grasp the dialectical relationship between technological empowerment and theoretical research, the core lies in clarifying the distinction between “substance” and “function”: Marxism serves as the “substance” that guides the direction, while digital-intelligence technology serves as the “function” that innovates the paradigm. In project approval and logical argumentation, the principle of problem-orientation rather than technology-orientation must be adhered to in topic selection, and the purely technological mindset of “looking for nails with a hammer” must be resolutely rejected. Genuine academic innovation requires not only pursuing the “novelty” of technological application, but also cultivating the “depth” of theoretical interpretation. Furthermore, a comprehensive approach combining qualitative interpretation with quantitative analysis must be upheld. A standardized digital-intelligence research paradigm should establish a framework through qualitative research, provide support through quantitative analysis, and then return to qualitative interpretation for theoretical elevation and value judgment — this is precisely the epistemological requirement of “practice, cognition, re-practice, and re-cognition.”

坚持马克思主义在技术应用中的指导地位。根本固者，华实必茂。马克思主义是我们立党立国、兴党兴国的根本指导思想。把握技术赋能与理论研究的辩证关系，核心在于厘清“体”与“用”之别：马克思主义为引领方向之“体”，数智技术为创新范式之“用”。在课题立项与逻辑论证中，必须坚持问题导向而非技术导向的选题原则，坚决摒弃“拿着锤子找钉子”的唯技术论思维。真正的学术创新，不仅要追求技术运用的“新意”，更要厚植理论阐释的“深意”。此外，必须坚持定性阐释与定量分析相结合的综合性路径。规范的数智化研究范式应以定性研究确立框架，以定量分析提供支撑，再回归定性阐释进行理论升华与价值判断，这正是“实践、认识、再实践、再认识”的认识论要求。

Establish academic norms for AI-assisted research using generative AI. In response to the academic crisis triggered by the involvement of generative AI in academic research, red lines for technological use must be drawn at the institutional level. On the one hand, a strict manual verification mechanism must be established to eliminate academic misconduct. In the face of AI’s “machine hallucinations,” any historical materials, data, and citations generated through algorithmic retrieval must be manually verified by the researcher against their original sources one by one. In Marxist theoretical research, the accuracy of citations directly concerns the scientificity of truth; machine-generated content that has not been verified must never be used as academic evidence. On the other hand, a declaration system for technological auxiliary use should be actively promoted to ensure academic transparency. Researchers should honestly state in their works the circumstances under which AI tools were used for auxiliary tasks such as data collation and text polishing, clearly delineating the boundary between human intellectual contribution and machine assistance. The core viewpoints, value judgments, and logical structure of the paper must be independently completed by the researcher, who bears full responsibility.

建立生成式人工智能辅助研究的学术规范。针对生成式人工智能介入学术研究所引发的学术危机，必须从制度层面划定技术使用的红线。一方面，需建立严格的人工核查确证机制以杜绝学术不端。面对人工智能的“机器幻觉”，凡经由算法检索生成的史料、数据与引文，研究者必须回归原始出处进行逐一的人工校验。在马克思主义理论研究中，引用的准确性直接关乎真理的科学性，未经确证的机器生成内容绝不能作为学术论据。另一方面，需积极推行技术辅助使用的声明制度，保障学术透明度。研究者应在论著中如实说明使用人工智能工具进行资料整理、文本润色等辅助工作的情况，明确划定人类智力贡献与机器辅助的边界。论文的核心观点、价值判断及逻辑架构，必须由研究者本人独立完成并承担全部责任。

Strengthen theoretical consciousness and political responsibility in the human-machine collaborative environment. The more intelligent the tool, the higher the comprehensive quality required of its user. Enhancing the subject status and capability of researchers is the fundamental strategy for addressing the normative crisis in the age of digital intelligence. Researchers in the New Era must both deepen their grounding in Marxist-Leninist classics and overcome technological fear, improving their composite literacy in using the scientific worldview and methodology of Marxism to harness big data. Only by mastering principles can one possess a pair of “discerning eyes” to sift truth from falsehood amid the vast sea of information junk and algorithmic ideological biases, activating the theoretical value of data. More importantly, no matter how technology evolves, researchers must always cultivate an academic sentiment that cares about reality and serves the people. Avoid indulging in self-amusement within the ivory tower of data; remember the original aspiration of scholarship for the people, writing scholarship into the hearts of the masses and putting theses on the land of the motherland. This value sentiment and sense of responsibility are precisely the enduring source of strength for Marxist theory, and are irreplaceable by any advanced algorithm.

强化人机协作环境下的理论自觉与政治担当。工具越是智能化，对使用者的综合素质要求越高。增强研究者的主体地位与能力，是应对数智时代规范危机的根本之策。新时代的研究者既要厚植马列原著功底，又要克服技术恐惧，提升以马克思主义科学的世界观和方法论驾驭大数据的复合型素养。只有精通原理，才能拥有一双“慧眼”，在海量信息垃圾与算法的意识形态偏见中去伪存真，激活数据的理论价值。更重要的是，无论技术如何迭代，研究者必须始终涵养关切现实与服务人民的学术情怀。切忌躲在数据的象牙塔里自娱自乐，要牢记学术为民初心，把学问写进群众心坎里、把论文写在祖国的大地上。这种价值情怀与责任担当，正是马克思主义理论历久弥新的力量源泉，也是任何高级算法都无法替代的。

Norms constrain one’s actions; standards guide one’s direction. Only by releasing the potential of technology within the framework of norms can Marxist research achieve tool empowerment and paradigm upgrading.

规以束其行，范以导其向。唯有在规范的框架中释放技术潜能，马克思主义研究方能完成工具赋能与范式升级。

IV. Conclusion

四、结论

The era is the mother of thought; practice is the source of theory. The arrival of the age of digital intelligence provides unprecedented opportunities for Marxist theoretical research, while also posing severe questions of the times. Digital-intelligence technology can assist researchers in broadening the horizon of text mining, improving the empirical validity of research, and enhancing the efficiency of knowledge production, injecting strong momentum into Marxist research. At the same time, it must be recognized that technological empowerment is accompanied by new challenges in cognitive methods, value orientations, and subject capabilities. The key to solving this epochal proposition lies in consistently upholding the guiding position of Marxism in technological application, transforming digital-intelligence technology — this “greatest variable” — into the “greatest increment” for theoretical innovation.

时代是思想之母，实践是理论之源。数智时代的到来，为马克思主义理论研究提供了前所未有的机遇，也提出了严峻的时代考题。数智技术能够辅助研究者拓宽文本挖掘视域、提高研究的实证效度、提升知识生产的效率，为马克思主义研究注入强劲动力。同时也要看到，技术赋能伴随着认识方法、价值导向和主体能力等方面的新挑战。破解这一时代命题的关键，在于始终坚持马克思主义在技术应用中的指导地位，将数智技术这一“最大变量”转化为理论创新的“最大增量”。

Standing at the historical intersection of the start of the “15th Five-Year Plan” [13]13 The “15th Five-Year Plan” refers to the 15th Five-Year Plan for National Economic and Social Development of the People’s Republic of China, covering the period 2026–2030. and the tenth anniversary of the “5·17” important speech [14]14 The “5·17” important speech refers to a speech delivered by Xi Jinping at a symposium on philosophy and social sciences on May 17, 2016, which called for building a system of philosophy and social sciences with Chinese characteristics., paradigm innovation in Marxist research should further focus on the following directions: First, in interdisciplinary methods, promote the deep integration of computational social science and dialectical materialism, exploring forward-looking deduction, digital-intelligence analysis, and virtual empirical research on classic texts. Second, in international comparative research, comparatively analyze the value orientations of Chinese and foreign AI tools and the dissemination paths of Marxism, elucidating how Marxism continuously develops itself in the age of digital intelligence by absorbing new knowledge and responding to new problems, thereby maintaining its scientificity and revolutionary character, and enhancing its external dissemination effectiveness. Third, in institutional norms and talent cultivation, build an academic norm system for the age of digital intelligence dominated by Marxism, and cultivate composite research talents with “classics grounding + tool operation capability + critical thinking.” Only by upholding the unity of “upholding fundamental principles” and “breaking new ground,” and achieving genuine academic freedom under the constraints of academic norms, can we accelerate the construction of an independent knowledge system for philosophy and social sciences with Chinese characteristics, Chinese style, and Chinese manner, produce outstanding academic achievements worthy of the great era, and inject strong theoretical momentum into comprehensively advancing the building of a strong country and the great rejuvenation of the Chinese nation through Chinese-path modernization.

站在“十五五”开局与“5·17”重要讲话十周年的历史交汇点上，马克思主义研究的范式创新需进一步聚焦以下方向：一是在跨学科方法上，推动计算社会科学与唯物辩证法的深度结合，探索面向经典文本的前瞻性推演、数智化分析、虚拟化实证；二是在国际比较研究上，比较分析中外人工智能工具的价值指向与马克思主义的传播路径，阐明马克思主义如何在数智时代通过汲取新知识、回应新问题以实现自身不断发展，始终保持其科学性和革命性，进而增强对外传播效能；三是在制度规范与人才培养上，构建马克思主义主导的数智时代学术规范体系，培养具备“原著功底+工具操作能力+批判思维”的复合型研究人才。唯有坚持“守正”与“创新”的统一，在学术规范的约束下实现真正的学术自由，方能加快构建具有中国特色、中国风格、中国气派的哲学社会科学自主知识体系，创作出无愧于伟大时代的优秀学术成果，为以中国式现代化全面推进强国建设、民族复兴伟业注入强大理论动能。