The current English education resource sharing platform has fragmented resource organization, insufficient granularity in user behavior modeling, and weak responsiveness of the scheduling feedback mechanism, resulting in limited resource aggregation accuracy, targeted recommendation ranking, and system distribution stability. Based on cloud computing architecture, this paper proposes a three-layer optimization strategy that integrates nested semantic tensor modeling, path attention sorting, and edge feedback scheduling control. This paper first uses multimodal tensors to semantically map and restructure resource content to unify resource expression; secondly, combines user behavior vectors with task path attention mechanism to construct a priority recommendation sequence to enhance the consistency of individual recommendations; finally, introduces load-aware index graph and minimum rescheduling cost function to realize dynamic resource path scheduling based on state feedback. Experiments show that the average response time of static load-aware scheduling is 229ms when the number of concurrent requests is 400, the resource matching accuracy of the fusion optimization model is 94.0%, and the node load variance under peak impact of the load level of this strategy is 30.1. This strategy achieves a systematic improvement in resource organization, service recommendation, and scheduling control based on the cloud computing architecture, enhances the platform’s sharing efficiency and operational stability, and provides solid support for the efficient integration of lifelong learning and intelligent English education resources.
