Considering the public sentiment is not comprehensively measured in the existing stock market prediction study, the study proposes a stock market prediction model using social sentiment analysis. First of all, a securities sentiment quantitative method based on heterogeneous graph model is applied for sentiment analysis on social media data, and thus quantified sentiment time sequence is obtained. Secondly, a prediction model based on self-organizing neural network is proposed for the stock index prediction by using sentiment sequence and the quotation index sequence. The experimental results on the domestic stock market and social media data sets show that the proposed model has improved by 15% and 12% over the BP (Back Propagation) neural network in the prediction error and accuracy respectively, which can better predict the stock market.