Deep Neural Network (DNN) are extremely vulnerable to adversarial examples, where subtle perturbations to legitimate inputs may cause the model to yield erroneous outputs. Exploring adversarial attacks can promote the robustness of deep learning models and advance the interpretability of DNN. Existing methods for generating Chinese adversarial examples typically employ simple transformation strategies, with emphasis on isolated Chinese linguistic features without considering the contextual effect of attacks. Hence, a heuristic-based algorithm known as the BSCA is proposed in this study. By comprehensively analyzing the linguistic variations and incorporating prior knowledge of Chinese character formation, phonics, and formality, a strategy for accurately assessing Chinese character deviations is designed. The adversarial search space is constructed based on this deviation strategy, and an improved beam search algorithm is utilized to optimize the generation process of Chinese adversarial examples in black-box attacks. Under strict constraints on perturbance and semantic deviation, BSCA can automatically adapt to different scenario requirements. Experimental evaluations conducted on TextCNN, TextRNN, and Bidirectional Encoder Representations from Transformers (BERT) for two Natural Language Processing (NLP) tasks indicate that BSCA can reduce the classification accuracy by at least 63.84 percentage points while incurring lower attack costs compared with baseline methods.