This paper focuses on intelligence course recommendations for college students' innovation and entrepreneurship education. Firstly, the traditional collaborative filtering algorithm was introduced, and then a new recommendation technique was designed based on an artificial neural network (ANN). The experimental data were collected through a crawler framework. The two methods were compared and analyzed. It was found that the training time of collaborative filtering and ANN was 16.78 s and 12.36 s, the testing time was 2.64 s and 2.12 s, the hit rate (HR) were 0.6078 and 0.6264, and the normalized discounted cumulative gain (NDCG) values were 0.2948 and 0.3356, respectively. The results reveal that ANN was more efficient in computation and better in recommendations. The results demonstrate the effectiveness of the ANN method for intelligent course recommendations. The method can be applied to the selection of innovation and entrepreneurship education courses for college students. Povzetek: Opisana je metoda nevronskih mrež za izbiro primernih študijskih predmetov.