Cloud computing technology is profoundly reshaping the operational modes and IT architectures of enterprises. However, as private cloud environments continue to expand and become more complex, their openness and dynamic nature introduce unprecedented security challenges. This paper thoroughly analyzed the security challenges faced by private cloud server environments and the limitations of existing technologies, and clarified the direction and objectives for improvement. Based on this analysis, an attention-based CNN-LSTM model using multi-model fusion strategy for private cloud environments is designed to enable prediction of network intrusion detection. Through a carefully designed feature extraction mechanism, key security features are extracted from multi-dimensional data including network traffic, system logs, and user behaviors. The proposed model performed higher accuracy and lower FPR than traditional model, which indicating better performance on identifying abnormal behaviors.