In this paper, firstly, the social network theory and features are studied in detail, and the key to improving the model prediction performance using the Bagging algorithm is to use the CART regression tree algorithm to make the variance as small as possible while ensuring that the average performance of the model does not drop significantly. Next, the random forest model was improved by using the feature importance measure algorithm and the F-measure weighting algorithm, which improved the overall performance of the random forest model. Then the elements of innovation and entrepreneurship ability of college students were identified, and the basic characteristics of the research subjects were obtained using the scale, and the influence of social networks on innovation and entrepreneurship ability based on the random forest model was studied empirically. The results show that the coefficients of the two dimensions of association strength and heterogeneity in the dimensions of social networks of college students are larger, 0.251 and 0.222, respectively, indicating that it is the network association strength and heterogeneity that have a greater impact on the improvement of innovative thinking ability. This study promotes innovation and entrepreneurship education in colleges and universities, which is important for the improvement of students’ innovation and entrepreneurship ability.