The study extracts the implied themes and mines the semantic attributes of the resources from the massive heterogeneous college innovation and entrepreneurship education resources through LDA topic model. And contextual dynamic clustering technology is added to instantly categorize the results and package the content-similar documents after each user retrieval. The model clustering identifies 6 major themes of innovative product service, team capability, marketing, resource funding, operation implementation and policy support. When the data volume reaches 70,000 documents, the F1 value of the fusion method in this paper is as high as 93.19%, while the traditional method is only 78.69%, leading by 18.43%. In terms of accurate retrieval, the overall checking accuracy for the six themes reaches 93.50%, meaning that more than 90% of the returned documents are truly relevant, while the traditional method’s checking accuracy is only 74.10%, and nearly 30% of the results are invalid noise. Students using the smart repository in this paper visited the repository an average of 202 times in a semester, which is more than 2.5 times of the 79 times of the control group. And the quality of learning outputs of students in the experimental class is higher, and the business plans they wrote scored 87.7±7.96 and 88.5±6.92 on the dimensions of innovativeness and feasibility, which achieved 13 to 21 points of improvement compared with the control group. The path of integrating semantic understanding and dynamic organization proposed in the study can effectively crack the problem of retrieval accuracy of educational resources, and ultimately translate into the power of enhancing students’ learning initiative and practical ability.
