Aiming at the single important word localization method and transformation strategy in the existing Chinese text adversarial example generation methods， which leads to the problem that it is difficult to improve success rate of the attack and the quality of adversarial examples， a multimodal adversarial example generation method for Chinese text classification was proposed from the perspectives of morphology， pronunciation， and semantics of Chinese characters. In the stage of calculating word importance， the mask model and model output were used to obtain confidence probabilities， and discrete nature of the predicted word was calculated as the sensitivity of the position， and finally the two were combined to determine the perturbation priority. In the adversarial transformation stage， a multimodal attack strategy combining the phonological and semantic features of Chinese characters was designed to generate the adversarial examples， and the candidate examples were generated by the lexicon， the Convolutional Neural Network （CNN）-based character pattern similarity comparison model and the Masked Language Model （MLM）. Experimental results show that the proposed method can achieve 33.2%-65.8% attack success rate against robust BERT （Bidirectional Encoder Representations from Transformers） and RoBERTa （Robustly optimized BERT pretraining approach） models. It can be seen that the generated adversarial examples can improve the robustness of the model through adversarial training.