To overcome the problem of low accuracy of traditional methods in the area of college student mobile learning strategy classification, a new classification model based on principal component analysis (PCA) and Elman neural network is proposed. First, dimensionality reduction was done to the obtained original data of student mobile learning strategies using PCA and 5 principal components were extracted to create a new feature sample matrix. Then the Elman neural network was trained and its generalization performance was tested. The simulation results indicate that: the classification accuracy of the single BPNN is 70.0%, the one of the single Elman model is 80.0% and the one of the PCAElman model is 100.0%; the PCAElman model can simplify the structure of the single Elman network, improve the training speed, classification accuracy and generalization performance; the effectiveness of the recommended model is proved.