The Multiple Signal Classification (MUSIC) algorithm, as a core branch algorithm in the field of array signal processing, has become a key research direction in array direction finding and signal parameter estimation. This study systematically investigates the impact of core parameters such as element spacing, snapshots, and signal-to-noise ratio on the performance of the MUSIC algorithm by adjusting these parameters and combining spectral peak feature analysis with root mean square error quantification. To address the issue of algorithm failure in complex environments, the study proposes three improvement methods, effectively enhancing the algorithm's performance.