Intelligent Recommendation System and Methods for Innovation and Entrepreneurship Courses

CN122335488APending Publication Date: 2026-07-03BAISE CITY NATIONAL HEALTH SCHOOL

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Applications(China)
Current Assignee / Owner: BAISE CITY NATIONAL HEALTH SCHOOL
Filing Date: 2026-03-31
Publication Date: 2026-07-03

AI Technical Summary

Technical Problem

Existing innovation and entrepreneurship course recommendations suffer from a lack of personalization, insufficient relevance, lack of dynamic adjustment, and insufficient adaptability, failing to meet the personalized needs and professional characteristics of vocational school students.

Method used

An intelligent recommendation system for innovation and entrepreneurship courses is constructed, including modules for data collection, data preprocessing, user profile construction, course resource library, recommendation engine, and result output. Combining improved collaborative filtering and content recommendation algorithms, a personalized course recommendation list is generated, and the operation requirements of different roles are realized through a permission management module.

Benefits of technology

It enables precise course recommendations, enhances the practicality and effectiveness of course learning, dynamically adjusts recommendation strategies to ensure timeliness and accuracy, and improves students' learning motivation and educational quality.

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Abstract

This invention discloses an intelligent recommendation system and method for innovation and entrepreneurship courses, belonging to the field of intelligent education technology. The system includes a data acquisition module, a data preprocessing module, a user profile construction module, a course resource library module, a recommendation engine module, and a result output module. The data acquisition module collects student, course, and interaction behavior data, and after preprocessing, constructs a multi-dimensional user profile containing static and dynamic features. The recommendation engine module combines an improved collaborative filtering algorithm and a content recommendation algorithm to generate an initial recommendation list based on the user profile and course feature data, and dynamically optimizes it based on real-time student interaction behavior. The result output module displays the recommendation results and collects feedback data. This invention sets up a professional adaptation optimization unit tailored to the professional characteristics of vocational schools, achieving personalized and precise course recommendations. It solves the problems of insufficient targeting and poor relevance in traditional recommendation models, and can dynamically adapt to changes in students' learning needs, improving the quality and effectiveness of innovation and entrepreneurship education.

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