Deep neural networks are vulnerable to attacks from adversarial samples. For instance, in a text classification task, the model can be fooled by modifying a few characters, words, or punctuation marks in the original text to change the classification result. Currently, studies of Chinese adversarial samples are limited in the field of natural language processing (NLP), and they fail to give due consideration to the language features of Chinese. This study proposes CWordCheater, a character-level and word-level high-quality method to generate adversarial samples covering the aspects of pronunciation, glyphs, and punctuation marks by approaching from the Chinese sentiment classification scenarios and taking into account the pictographic, alphabetic, and other language features of Chinese. The ConvAE network is adopted to embed Chinese visual vectors for the replacement modes of visually similar characters and further obtain the candidate pool of such characters for replacement. Moreover, a semantic constraint method based on universal sentence encoder (USE) distance is proposed to avoid the semantic offset in the adversarial sample. Finally, the study proposes a set of multi-dimensional evaluation methods to evaluate the quality of adversarial samples from the two aspects of attack effect and attack cost. Experiment results show that CWordAttacker can reduce the classification accuracy by at least 27.9% on multiple classification models and multiple datasets and has a lower perturbation cost based on vision and semantics.