In the realm of data mining based on modern acupuncture clinical research, the impact of literature features such as literature quality, evidence level, sample size, and clinical efficacy on the quality of data mining outcomes remains uncertain. These issues are significant factors restricting the translational application of data mining research results. We suggest employing both entropy weight and linear weighting techniques to assess the specified indicators. This assessment results in a comprehensive weighted score for acupuncture prescriptions, serving as the foundation for our ensuing data mining endeavors. In this study, migraine research serves as an example to contrast the efficacy of weighted algorithms against that of classical algorithms. The findings demonstrate that the algorithm introduced in this research significantly contributes to studies focusing on the dispersed selection of acupuncture points. Its superiority lies in cluster analysis, where it adeptly discerns potential patterns in the amalgamation of acupoints. This algorithm amalgamates evidence-based acupuncture with data mining processes, providing innovative perspectives that augment the caliber of research in acupuncture data mining. Nonetheless, additional research is essential to corroborate these results.