Application and Research of Multi-Layer Network and Time Series Analysis in Optimizing Business Environment Indicators

In the current complex and ever-changing global economic situation, optimizing the business environment has become a key factor in promoting high-quality economic development. In order to improve the quality of China's business environment, this study uses multi-layer networks and LSTM (Long Short Term Memory) models to optimize business environment indicators such as market access, regulatory efficiency, legal protection, innovation support, and talent attraction. This study first establishes a theoretical framework and clarifies the goals and directions for optimizing business environment indicators. Subsequently, the study collects multidimensional data related to market regulation, administrative procedure approval time, legal case processing efficiency, research and development investment, and talent flow, and conducts data cleaning and preprocessing. Next, this study constructs a multi-layer network model that takes various dimensions of the business environment as network layers, analyzes the interactions and influences between these dimensions, and studies the performance and trends of these indicators at different time points through time series analysis. The experimental results show that the overall network density has increased by about 35%, and the root mean square error (RMSE) based on the LSTM model is 1.43. In the above data conclusions, the combination of multi-layer networks and LSTM model analysis helps identify key optimization points and enhance the scientific and effective policy-making.