Abstract: To enhance the scientific rigor and reliability of risk assessment, this study developed a comprehensive evaluation methodology based on fractal dimensions, validated and demonstrated using long-term microseismic monitoring data from the Xiaojihan Coal Mine. The proposed methodology enables the quantitative identification of precursory signatures in acoustic emission (AE) and microseismic (MS) activities linked to impending rock failures. Model parameters were calibrated using 15 months of preprocessed historical MS data from the mine, and the method was subsequently applied for dynamic risk assessment during ongoing mining operations. The established fractal dimension system comprises six information dimensions and three capacity dimensions, forming a multi-attribute characterization framework. A Gaussian membership-based Fuzzy Comprehensive Evaluation (FCE) model translates the analytical results into probability distributions across four risk levels: "none, weak, moderate, and strong," ultimately yielding a deterministic comprehensive risk rating. Results indicate that this fractal dimension-based methodology effectively captures precursor characteristics of rockbursts. The assessment outcomes align well with field observations, demonstrating its potential as a reliable technical tool for seismic hazard prevention and control in mines.