A study of the impact of corporate market forecasting capability on the accuracy of strategic decision-making based on computational analysis of business data

This study focuses on the computational analysis of business data, constructs a market prediction framework that integrates K-means clustering, feature standardization and improved N-BEATS model, and verifies its effect on the accuracy of enterprise strategic decision-making based on multi-source data.The study selects real-time transaction data and weather data from 800 merchants under Alibaba, extracts key features through standardization and correlation analysis, and improves the model by introducing topological features and multi-attention mechanism, which significantly optimizes the time series prediction accuracy and reduces the RMSE by 18.6%.The empirical analysis for tissue paper category shows that the forecast error rate of the time series decomposition method is only 0.58%, which is better than the traditional trend method and seasonal index method.Through the regression analysis of 328 business managers' questionnaires, data-driven analysis =0.617, p<0.001 and innovative forecasting =0.594, p<0.001 have a significant positive effect on strategic decisionmaking accuracy and consensus.