Traditional market competition analysis methods struggle to capture complex competitive and cooperative relationships between enterprises. To address this, this study constructs an AI-based network analysis model for enterprise market competition. First, the enterprise competition system is abstracted as a directed weighted graph, and the competitive intensity between enterprises is quantified from dimensions such as market overlap degree, technological similarity, and resource competition degree, with weight coefficients optimized via a multi-objective genetic algorithm (MOGA). Second, the hierarchical information propagation mechanism of graph neural networks (GNNs) and a competitive intensity-aware attention mechanism are employed to extract features from the competition network. Finally, a competition trend prediction and key competitor identification model is constructed by integrating bidirectional long short-term memory (Bi-LSTM) networks and a temporal attention mechanism. Experimental results show that the model achieves a weighted mean squared error of 0.098 in market share prediction tasks and a top-5 recall of 0.85 in key competitor identification, improving prediction accuracy compared to traditional methods while reducing identification time from weeks to hours. This effectively enhances the ability of enterprises to analyze and predict dynamic competition trends.
Li et al. (Wed,) studied this question.