Contemporary MBA education faces a significant contradiction between increasing demands for personalized learning experiences and the limitations of traditional teaching methodologies. To address this challenge, this paper presents the design and implementation of an innovative MBA personalized teaching system powered by deep learning technology. The system uniquely integrates collaborative filtering algorithms with LSTM neural networks to create three core functional modules: intelligent recommendation engine, comprehensive learning analytics, and dynamic personalized learning path generation. Through extensive system deployment and practical application, the results demonstrate significant improvements in MBA teaching quality and learning efficiency. The average student engagement increased by 45%, while learning outcomes showed a 38% improvement compared to traditional methods. This research provides valuable insights and practical frameworks for the digital transformation of MBA education, offering a novel approach to educational reform that balances personalization with pedagogical effectiveness.