Collaborative Path Optimization for Technology-Driven Innovation in the Sports Industry Using Enhanced Graph Attention Networks

This paper adopts a collaborative development path analysis method based on Graph Attention Networks (GAT). A collaborative network is constructed by treating entities in the sports industry as nodes and their relationships as edges. The multi-head attention mechanism and residual connections are used to dynamically adjust connection weights, enhancing model expressiveness and stability. The optimized model identifies key collaborative paths and improves resource allocation efficiency. Experimental results show that the accuracy reaches 94.6%; the path length of scientific research institutions is reduced by about 33%; the number of cooperative achievements increases by 87.5%, verifying the feasibility of this approach. The method provides technical support for optimizing resource allocation and promoting collaborative innovation in the sports industry.