RISC-V is rapidly evolving, with the porting and development of its software ecosystem advancing. Due to the complexity of operating system itself, performance evaluations are often limited to the module level, making it difficult to conduct systematic analysis at the function level. The software adaptation of RISC-V often draws on optimization strategies from mature architectures like ARM. This study proposes a method for fine-grained kernel performance evaluation and defect detection based on cross-architecture comparison. The method shifts the evaluation focus from the module level to the function level by using cross-architecture context matching and architecture-specific anomaly detection. It was applied to the Linux 5.10 kernel to compare the performance of RISC-V and ARM, which revealed the limitations of RISC-V. Experiments using a targeted test suite identified 9 performance issues from 25 anomalous contexts on RISC-V, with 80% classified as high priority. The result demonstrates that the proposed method achieves an evaluation accuracy of 80%, indicating the effectiveness and accuracy of the method in identifying performance issues during architecture adaptation.