This chapter elaborates on the fundamental theories and model construction process of fuzzy pattern recognition. Firstly, it expounds the core concepts of fuzzy mathematics, including basic knowledge such as fuzzy sets, membership degrees and fuzzy relations, laying a theoretical foundation for model establishment. Subsequently, it conducts a comparative analysis of the limitations of the Analytic Hierarchy Process (AHP) and the multi-level fuzzy comprehensive evaluation method, pointing out their problems of subjective weighting and complex calculation. The focus is placed on constructing a fuzzy pattern recognition model for coalbed methane target area optimization: first, perform normalization processing on three types of parameters—qualitative parameters, positively correlated quantitative parameters and negatively correlated quantitative parameters—to eliminate differences in dimensions and magnitudes; then construct the evaluation parameter matrix and the four-level evaluation grade matrix and convert them into column vectors; finally, calculate the fuzzy closeness degree using the cosine similarity of included angles, and determine the development potential grade of the block to be evaluated by comparing the closeness degree values. This model eliminates the need for subjective weighting, simplifies the calculation process, and effectively improves the objectivity and accuracy of evaluation results.