Research on Personalized Art Learning Path Recommendation Algorithm Based on Reinforcement Learning

Abstract

In online art education, the learning path has strong non-linear dependence and significant individual differences. Traditional sequential recommendation methods are inefficient because they struggle to dynamically model the skill topological constraints. This paper proposes a personalized art learning path recommendation algorithm named Art-DQN, which integrates graph attention networks and improved deep Q networks. Firstly, an art skill directed topological graph is constructed, and the graph attention network is used to extract high-order neighborhood features of the learner's skill state; Secondly, a dual-channel feature fusion module is designed, combining the skill graph features with the behavior sequence features encoded by LSTM and inputting them into the Q network; Finally, an ε-greedy exploration strategy based on skill topological constraints and a prioritized experience replay mechanism are proposed. Experiments on a dataset containing 500 real learners' logs and 1000 simulation trajectories show that the average skill mastery time of Art-DQN is 32.4 steps, which is 22.3% lower than that of the standard DQN, the target skill achievement rate reaches 92%, the number of prerequisite violations is 0, the path redundancy is as low as 0.09, and the convergence speed is increased by 41%. Ablation experiments verify the independent contributions of the dual-channel fusion and dynamic reward weights, and parameter sensitivity analysis and robustness tests further prove the stability of the algorithm. This algorithm provides an effective solution that combines structural constraints and personalized adaptation for intelligent recommendation in art education.