A huge amount of stock reviews occurred on the Internet due to its rapid development, therefore, the stock reviews sentiment analysis has profound significance for the study of the financial market. Due to the lack of a large amount of labeled data, the accuracy of existing sentiment analysis of Chinese stock reviews remains to be further improved. In this paper, a sentiment analysis algorithm for Chinese stock reviews based on BERT is proposed and it improves the accuracy of sentiment classification. The algorithm uses BERT pre-training language model to perform representation of stock reviews on the sentence level, and then input the obtained feature vector into the classifier layer for classification. In the experiments, we show our method has nearly 8% and 9% improvement than TextCNN and TextRNN in F1, respectively. Our model can obtain the best results via fine-tuning which is proved to be effective in Chinese stock review sentiment analysis.