With the rapid development of the Internet, the public can freely express personal opinions on medical events and doctor-patient relationships through the Internet, which are of the correct value for guiding the public. Orientation has great research significance. However, the topic analysis algorithm that only considers single-modal data cannot accurately grasp the truth of the entire public opinion event, and there are problems such as inaccurate topic extraction and preconceived personal emotions. To solve this problem, this paper proposes a LDA-based multimodal data topic analysis algorithm, named MD_LDA(multimodal data topic analysis Based on LDA). The multimodal topic analysis is calculated by the decision-level fusion of the results of each modal topic analysis. As a result, it further solves the defect that traditional methods do not fully consider multimodal data. The experimental results show that for multimodal public opinion events, the proposed MD_LDA algorithm is better than the algorithm for topic analysis of single-modal data in terms of the extraction effect of topic words. Compared with the traditional keyword extraction algorithms TF_IDF and TextRank, the accuracy of the MD_LDA algorithm and the extraction efficiency of subject words are improved, which proves the effectiveness of the MD_LDA algorithm for subject analysis combined with multimodal data.