This work aims to explore the adaptive learning algorithm in the mobile learning environment, so as to improve learners’ learning efficiency and satisfaction. Through in-depth analysis of the theoretical basis of adaptive learning, this study defines the key components of adaptive learning system, and constructs an individualized adaptive learning system based on these theories. In this article, hierarchical architecture is adopted to design the system. By collecting and analyzing learners’ behavior data, the learner model and knowledge model are constructed, and then the dynamic adjustment of learning content and strategies is realized. In the experimental assessment stage, this article selects learners from different backgrounds to conduct empirical research and uses statistical analysis and ML (Machine learning) assessment index to quantitatively evaluate the performance of the algorithm. The results show that the adaptive learning algorithm can significantly improve learners’ learning efficiency and grades, and at the same time improve learners’ satisfaction with the mobile learning platform. This study not only verifies the effectiveness and feasibility of adaptive learning algorithm in mobile learning environment, but also provides a new idea for the innovation and development of educational technology.