E-Commerce Demand Forecasting Model and Market Dynamic Regulation Algorithm Based on Big Data Analysis

In the field of e-commerce, accurately predicting market demand and efficiently adjusting market strategies are crucial for enterprises to maintain competitiveness. This article first constructs an e-commerce demand forecasting model that integrates historical sales data, user behavior data, and market trend information, using Long Short Term Memory (LSTM) as the core algorithm to capture long-term dependencies in time series data. Through feature engineering and data preprocessing, the model can provide high-precision demand forecasting. Then, this paper designs a dynamic regulation algorithm based on demand forecast results and market feedback, which integrates the idea of reinforcement learning (RL), and gradually optimizes the regulation strategy through trial and error and learning. Empirical analysis shows that, compared with traditional time series forecasting methods, LSTM model has significant advantages in forecasting accuracy, and the market dynamic control strategy combined with RL algorithm can effectively improve enterprise profits and control inventory costs. This study provides scientific decision support for e-commerce enterprises and promotes their sustainable development.