Intelligent Decision-Making Based on Computer Intelligent Algorithms and Big Data Technology

With the surge in data volume and dimensions, existing decision-making systems are difficult to capture nonlinear relationships and cope with the complexity and diversity of data, which leads to decision lag. To solve this problem, this article studies an intelligent decision-making system. The system integrates multi-layer feedforward neural networks and decision tree algorithms, and designs a distributed computing architecture based on the Apache Spark platform. Through the integration of the big data platform, the system integrates and processes multiple data sources; then, a three-layer feedforward neural network model is adopted, and a specific mechanism is used to prevent overfitting. After that, the decision tree algorithm is used to deeply explore the complex relationships in the data; Apache Spark is applied to accelerate the calculation process and efficiently query and analyze the stored data. The system also supports online learning strategies to continuously update the model to adapt to new data changes. The research results show that in the field of financial risk analysis, the average accuracy of the intelligent decision-making system in this article is 92.29 %, and the average response time is only 0.013 seconds; the average accuracy and response time of the traditional system are 82.54 % and 0.034 seconds respectively; the system in this article is significantly better than the traditional system. According to the study, an intelligent decision-making system not only increases decision-making's efficiency and accuracy but also offers solid base for more intelligent decisions in the future.