The convolutional neural network is used to identify eight diseases of rice leaf, including dry tip nematode, bacterial leaf blight, bacterial stripe disease and so on, to realize the automatic recognition of multiple rice diseases by computer vision. After the disease images were expanded through random rotation, random change of brightness and contrast, and so on, 80% of the images were randomly divided into training samples and 20% were divided into test data. The training samples were directly input into the AlexNet and LeNet5 networks for training, and the AlexNet and LeNet_models were obtained. FCM_model and BN_model are obtained using two methods of image recognition on AlexNet network: fuzzy C-means clustering image processing and batch normalization layer after activation function of each layer. From the identification results of the four models and the analysis of model performance evaluation indexes, it can be seen that the BN_model has the best recognition effect. The BN_model has a final recognition rate of 99.11%, which is increased by percentage points of 0.23, 0.59,4.43 than AlexNet_model, FCM_model, and LeNet_model, respectively. The model has strong recognition and generalization ability, which provides reference for the research of rice diseases based on convolutional neural network.