In the present day’s education, building and recommending individualised learning plans need to be carried out to improve learning efficiency and quality. Individualised learning refers to modifying the content and methods of teaching based on different needs, interests, speeds and abilities of various learners. This paper will introduce the foundation of personalised learning paths and examine how machine learning is applied in education to customise students’ learning experiences, such as the construction of personalised recommendation systems and learning path design. Path recommendation is based on learner feature modeling, the construction of a learning resource database, the optimisation of recommendation algorithms, the generation of learning paths, and the guarantee of system scalability to form an intelligent algorithm framework. Test Cases for the Algorithm are shown below.
