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Effective fault detection of private cloud systems is of great practical significance to ensure the stability of IT systems and to carry out reliability information activities. To this end, starting from the historical trend data of private cloud systems, a hybrid model of CNN-LSTM-PSO based on particle swarm optimization (PSO) is proposed by combining convolutional network (CNN) and long short-term memory (LSTM) recurrent neural network., to realize fault detection of private cloud. The X11 algorithm and other technologies are used to preprocess the data, the CNN network is used to extract the relevant feature information of the time series data of the monitoring indicators, and the CNN-LSTM prediction model is established by training the LSTM network parameters, and the PSO algorithm is designed to optimize the parameters of the prediction model and reduce the Small prediction error, and the threshold range is determined by Gaussian normal distribution to achieve accurate fault detection. Compared with the traditional single prediction model and some existing combined prediction models, the root mean square error, mean absolute error and mean percentage error of the predicted results of the CNN-LSTM-PSO model are lower than those of the other models. The experimental results verify that the model has higher accuracy and faster prediction speed in prediction effect, and has good results in both accuracy and real-time performance in fault detection of private cloud.