In order to improve the reliability of software, software defect prediction has become an important research direction in the field of software engineering. Traditional software defect prediction methods mainly design static code metrics and use machine learning classifiers to predict the defect probability of the code. However, the static code metrics do not fully consider the semantic features hidden in the code. According to this situation, this study proposes a software defect prediction model based on convolutional neural network. First, extract the characterization vectors from the appropriate nodes in the abstract syntax tree of the source code, and construct a dictionary to map them to integer vectors to facilitate input to the convolutional neural network. Then, a convolutional neural network is designed based on GoogLeNet, and the ability of the convolutional neural network to deeply mine data is used to fully mine the grammatical and semantic features of the features. In addition, this model uses the method of random oversampling to deal with the imbalance of data, and uses the method dropout in the network to prevent the model from overfitting. Finally, the historical engineering database on Promise is used to test the model, and AUC and F1-measure are used as indicators to compare with the other three methods. The results show that the proposed model has a certain improvement in software defect prediction performance.