Research on Strategies for Cultivating College Students’ Innovative Thinking and Improving Their Entrepreneurial Abilities in the Artificial Intelligence Environment

Digital Technologies Research and Applications

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Research on Strategies for Cultivating College Students’ Innovative Thinking and Improving Their Entrepreneurial Abilities in the Artificial Intelligence Environment

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https://doi.org/10.54963/dtra.v5i1.2192
Xiao, Y., & Tang, Q. (2026). Research on Strategies for Cultivating College Students’ Innovative Thinking and Improving Their Entrepreneurial Abilities in the Artificial Intelligence Environment. Digital Technologies Research and Applications, 5(1), 277–296. https://doi.org/10.54963/dtra.v5i1.2192
Volume 5 Issue 1: March 2026 (in progress)
Copyright (c) 2026 Yan Xiao, Qiang Tang
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Authors

  • Yan Xiao

    College of State Governance, Southwest University, Beibei District, Chongqing 400715, China
  • Qiang Tang

    International College, Rattana Bundit University, Bang Kapi District, Bangkok 10240, Thailand

Received: 29 December 2025; Revised: 5 February 2026; Accepted: 6 March 2026; Published: 20 March 2026

Higher education continues to emphasize theoretical knowledge, leaving students ill-equipped to translate technological advances into viable business solutions. This gap often results in innovation and entrepreneurship projects with limited technological depth or poor execution, ultimately hindering the overall growth index. Therefore, this paper studies the strategies for cultivating college students’ innovative thinking and improving their entrepreneurial abilities in the artificial intelligence environment. Using a deep reinforcement learning algorithm, we encode both structured data (e.g., course grades) and unstructured data into a unified state space. Through the interaction between the agent and the environment, the reward mechanism is used to optimize and fuse actions, combined with the deterministic policy gradient to fuse multi-source heterogeneous data. The K-means clustering algorithm is used to cluster the results of multi-source heterogeneous data fusion, and college students are divided into different characteristic groups. The Bayesian network combined with the expectation maximization algorithm is used to evaluate the innovation and entrepreneurship abilities of college students with different characteristics by initializing the prior probability and iteratively optimizing the posterior probability of the latent variables. According to the evaluation results of college students’ innovation and entrepreneurship abilities, a user-strategy feature matrix is constructed based on the collaborative filtering algorithm of deep learning Euclidean embedding. Through the multi-layer perceptron to learn the non-linear interaction, corresponding strategies for cultivating innovative thinking and improving entrepreneurial abilities are provided for students. The experimental results show that when this method is used to cultivate college students’ innovative thinking and improve their entrepreneurial abilities, the innovation and entrepreneurship growth index is as high as 0.9, providing a reference for colleges and universities to cultivate high-quality innovation and entrepreneurship talents.

Keywords:

Artificial Intelligence College Students Cultivation of Innovative Thinking Improvement of Entrepreneurial Ability Deep Reinforcement Learning Bayesian Network

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