Abstract: A method for generating personalized learning path based on a metaheuristic algorithm is disclosed, comprising: initializing a candidate solution population; using a concept coverage function, a time penalty function and a style matching function, constructing a multi-objective fitness function to comprehensively score the candidate solutions; locally updating each candidate solution in the population and updating an expert age, followed by globally updating the candidate solution population by a dynamic control and a migration mechanism. When a set iteration count is reached, calculating and ranking a priority score of the learning material, and generating a personalized learning sequence by utilizing a weighted pooling method.