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This invention discloses a dynamic load balancing adjustment method for big data processing that integrates deep learning. The method comprises the following steps: Step S1: Collecting basic parameters of edge nodes and calculating communication and collaboration metrics between edge nodes; Step S2: Constructing an initial polyhedron structure based on the basic parameters, communication metrics, and collaboration metrics of the edge nodes; Step S3: Obtaining real-time load data of the edge nodes and predicting future load data of the edge nodes based on the real-time load data using an LSTM model; Step S4: Optimizing the initial polyhedron structure based on the future load data using the Transformer deep learning algorithm to obtain an optimized polyhedron structure; Step S5: When an edge node experiences overload, formulating an adjustment strategy based on the structural parameters of the optimized polyhedron structure and performing load balancing adjustments based on the adjustment strategy. By constructing a dynamically changing polyhedron structure, the method reflects changes in edge node load and inter-node relationships, enabling timely and accurate dynamic load balancing adjustments.