Application of Intelligent Algorithm in Enterprise Competitive Strategy Analysis and Prediction

This study investigates the possible uses of intelligent algorithms within competitive strategies analysis and prediction of an enterprise. Due to digital transformation and heavy global competition, traditional methods of performing strategic analysis do not offer adequate insights into emerging dynamics of a market and future trend forecasts. We develop a strategy that attempts to apply machine learning, deep learning, and fuzzy logic algorithms to enterprise strategy theory-constructing a feature engineering system aimed at competitive strategies, developing a hybrid intelligent algorithm model that processes both structured and unstructured strategic data simultaneously, and creating a competitive strategy prediction evaluation system. Empirical analysis indicates that the ensemble hybrid model, as compared to traditional analytical methods, surpasses the 91.7<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> benchmark accuracy rate in strategy classification within various industries. Additional findings revealed that the relationships between strategy and performance are strongly moderated by industry type where some effects differ within the technology hardware, retail apparel, and financial services sectors. With more advanced industry-specific trajectory analysis, our model allows faster anticipation of shifts in competitor strategies and guides proactive strategy changes rather than reactive, providing clearer timelines for strategic renewal. Computational intelligence and strategic management have not been integrated into one field of research-that gap is filled with this study alongside the reasoning of providing tools adaptable by enterprises to deal with competition in sophisticated business settings.