With the increasing diversity of 5G service types and the intensifying dynamic fluctuations of network load, achieve differentiated quality of service assurance in a network slicing environment has become a key issue in resource management. To address this problem, this paper proposes a deep reinforcement learning mechanism for 5G network slicing quality of service assurance based on the traditional proximal policy optimization actor-critic framework. First, the slicing resource allocation is modeled as a constrained Markov decision process, jointly considering the collaborative optimization of bandwidth, computing, and wireless resources. Meanwhile, a graph attention network and bidirectional long short-term memory are introduced to extract topological correlations and temporal service features, combined with an adaptive Lagrangian penalty and dynamic reward shaping mechanism, to comprehensively optimize delay, throughput, reliability, fairness, and slice isolation performance. Experimental results show that the proposed method outperforms existing baseline models in terms of quality of service satisfaction rate, delay control, resource utilization, and convergence stability.

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