Research on the Construction of Intelligent Data Evaluation System Based on Network Communication Evaluation Model in Cloud Computing Environment

This paper proposes an innovative network communication performance prediction algorithm based on deep learning, and designs an intelligent data evaluation system for real-time network performance evaluation in cloud computing environment. By analyzing the network traffic data in cloud computing environment and using deep learning model, the system can accurately predict key network performance indicators such as bandwidth, delay, and packet loss rate. The proposed algorithm combines the deep learning architecture of MLP and CNN, thereby improving the prediction ability of complex network environment. Experimental results show that under different network load and bandwidth conditions, the model can maintain a high prediction accuracy, and the prediction error is controlled within 5%, which is about 20% higher than the traditional network communication evaluation method. In addition, the designed intelligent data evaluation system can reflect the dynamic changes of network performance in real time and has strong adaptability. Experiments show that the system can maintain good performance in complex environments with high concurrency and low latency, and can provide real-time and accurate performance feedback for the optimization and maintenance of cloud computing platforms.