This study explored an improved English language evaluation algorithm for virtual learning environments, particularly in metaverse educational settings. By integrating big data analysis with fuzzy measure theory, the model extracted learners’ language usage habits, progress, and difficulties from large datasets, overcoming the limitations of traditional subjective evaluation methods. Fuzzy measures quantified subjective factors such as clarity, fluency and intonation, while a Sugeno integral approach combined these measures into an overall score. Comparisons with traditional methods have shown significant improvements in the assessment of speaking skills across a range of proficiency levels.