Text sentiment classification is a hot topic in the field of natural language processing. One of its important applications is to dig out important information from online comments and grasp the trend of public opinion on the Internet. Therefore, this study proposes a method of text sentiment classification based on GDBN neural network. The algorithm improves the hidden layer in the DBN neural network by introducing genetic algorithm, which is of powerful global searching ability, and the algorithm optimizes the number of hidden units and obtains the appropriate value of the current model, then the modeling and feature extraction of this model. Finally, we can classify the extracted features of the BP neural network. By testing multiple data, the results show that the proposed algorithm is effective.