In this study, we propose an intelligent identity authentication method for an optimized convolutional deep belief network to address the information security problem faced by smart phones. First, the collected raw data is preprocessed and then input into the sparse autoencoder for pretraining. The pretrained weight is used as the convolution kernel of convolutional deep belief networks, and the layer-by-layer greedy algorithm is adopted to formally train the model. Subsequent to the training, the extracted features are integrated with the root mean square layer, and the weight between the root mean square layer and the output layer is adjusted using the supervised learning algorithm. Finally, the classification results are output through the Softmax classifier. The proposed method can directly process high-dimensional gesture data and establish a gesture model for feature extraction. Simulation results show that compared with the hidden Markov algorithm and the deep belief network algorithm, the proposed method can significantly improve the accuracy of identity authentication.