In the design of personalized recommendation algorithms for vocational education resources, chaotic mapping is commonly used to construct recommendation learning models. However, such models are susceptible to the curse of dimensionality, leading to suboptimal NDCG ( Normalized Discounted Cumulative Gain). To address this, an optimized design of a personalized recommendation algorithm is proposed for vocational education resources based on dynamic knowledge graph and reinforcement learning. Specifically, a dynamic knowledge graph representation is constructed from multi-source heterogeneous vocational education resource data, and a temporal-aware embedding representation vector is introduced to derive dynamic embedding functions. A graph attention network is employed to aggregate neighborhood information of entities, updating node representations to deeply integrate structural information of the dynamic knowledge graph. An Actor-Critic framework is adopted to maximize cumulative expected rewards, and entropy regularization is introduced to achieve a composite reward function that balances recommendation objectives. By combining a cost-sensitive search algorithm with the reinforcement learning strategy, a heuristic evaluation function is generated. A resampling mechanism is applied to locate effective resources within the dynamic knowledge graph for path insertion, enabling adaptive recommendation path planning and achieving personalized recommendation of vocational education resources.Experimental results demonstrate that the proposed algorithm consistently achieves NDCG values above 0. 8 across different types of resources. It effectively accounts for item relevance and ranking positions, placing items of higher user interest in more prominent positions. The recommendation quality remains high without being affected by browsing and filtering delays or demand mismatches, exhibiting significant recommendation consistency.

dynamic knowledge graph, reinforcement learning, vocational education resources, resource recommendation, recommendation algorithm