Mini programs are required to provide privacy policies to inform users about the types and purposes of the privacy data being collected and used. However, inconsistencies between the underlying codes and the privacy statements may occur, potentially deceiving users and leading to privacy leakage. Existing methods for detecting such inconsistencies typically rely on converting the code and policies into predefined labels for comparison. This approach introduces information loss during label conversion, resulting in underreporting. In addition, traditional code analysis methods are often ineffective against obfuscated mini program code. To address these limitations, a semantic-analysis-based method for code-to-policy consistency detection in mini programs is proposed. Customized taint analysis is utilized to capture code behaviors based on mini program coding paradigms, and a code language processing model is applied to represent these behaviors as natural language descriptions. By aligning the natural language representation of code behaviors with the stated purposes in privacy policies, expert reviewers can analyze the consistency between the two effectively. Experiments indicate that the proposed taint analysis module covers all three data return methods and four common data flow patterns within mini programs APIs, achieving superior sensitivity compared to existing methods. Semantic analysis of tens of thousands of mini programs reveals privacy leakage risks associated with certain high-frequency API calls. Case studies using the MiniChecker tool further identify real-world instances of mini programs where inconsistencies between code and privacy policies are detected.