This study integrates a knowledge graph into a model for learning resource recommendation considering the logical relation between knowledge points, aiming to address the “cognitive overload” and “learning trek” in online learning and meet the users’ personalized learning needs. Firstly, a knowledge graph, a learning resource model, and a user-oriented mathematical model are developed. Then, we establish a multi-objective optimization model by taking into account the user preference and the correlation between the users’ knowledge base and the knowledge points covered by the learning resources. After that, this model is solved by the Adaptive Multi-Objective Particle Swarm Optimization (AMOPSO). Furthermore, we reduce the size of the external population through sorting the individual crowding distance in a descending order, thus obtaining the two-object Pareto frontier with optimal distribution and the recommended resource sequence. The proposed algorithm is also compared with the standard multi-objective particle swarm optimization and evaluated by HV and IGD, demonstrating its robust diversity, stability, global optimization, and convergence. Finally, five-fold cross-validation verifies the recommendation from the proposed algorithm.