The problem of inappropriate suggestion placement exists, even great importance in classical Chinese culture. Conventional genetic algorithms are unable to address inherent challenges within this cultural context, leading to suboptimal results. Therefore, upon analyzing the existing system, this research proposes a tailored recommendation-driven learning system for classical Chinese culture. Initially, the memory module theory is utilized to pinpoint influential variables, and these metrics of the learning to minimize disruptive elements. Subsequently, the memory chip theory is employed to craft a personalized recommendation learning system plan, followed by a thorough analysis of educational outcomes. MATLAB simulation results indicate that, against specific evaluation standards, customized recommendations outperform traditional genetic algorithms in terms of understanding system certainty and timeliness of contributing factors to the instructional system.