Optimization of Complex Systems Based on LSTM Neural Networks and Random Forests

In order to enhance optimization and prediction in complex systems, this paper combines several advanced machine learning algorithms for in-depth analysis and modeling. First, this paper collects relevant data from 2010 to 2023 and preprocesses the data by performing EDA (Exploratory Data Analysis) to ensure the completeness and accuracy of the model input data. EDA provides an effective means of data cleansing through graphical, distributional, and statistical analyses that reveal potential patterns and trends in the data. Next, this paper uses the random forest algorithm for feature importance analysis. Random Forest is an integrated learning method that improves the accuracy of a classification or regression task by constructing multiple decision trees and incorporating majority voting. In this analysis, Random Forest determines the weight relationship between key factors and target variables, helping to filter out the main factors affecting the performance of the system. In the construction of the prediction model, this paper adopts the LSTM (Long Short-Term Memory) neural network, which, with its advantage of being good at dealing with time-series data, is able to capture the long-term dependencies in the data, and is suitable for the accurate prediction of long-term trends and behaviors in complex systems. The prediction model constructed based on LSTM further improves the time-series prediction ability of the data and solves the shortcomings of the traditional methods in long-time dependency processing. In addition, this paper proposes a quantitative evaluation system for the optimization problem, which combines key features in multiple dimensions. The correlation between different features is revealed through heat map analysis, and K-mean clustering with random forest weighted analysis is used to classify the data. Subsequently, the data with different features were hierarchically ranked using TOPSIS comprehensive evaluation method, and the optimization model of the optimal strategy was constructed. TOPSIS is a multi-criteria decision-making based evaluation method, which can help decision makers to select the solution that is closest to the ideal solution. In this paper, through the comprehensive use of machine learning algorithms such as EDA, Random Forest, LSTM and TOPSIS, we not only improve the prediction and optimization effect in complex systems, but also provide a strong algorithmic support for future intelligent decision-making. The study shows that the multi-model combination strategy based on machine learning can effectively deal with the uncertainty and diversity in complex data analysis, showing a wide range of application prospects [1]–[3].