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Haze prevention and control is a hot topic of air quality protection research at present, and PM2.5 concentration prediction is one of the keys to haze prevention and control. In this paper, a dual system co-evolution gene expression programming algorithm (DSCE-GEP) was used to predict PM2.5 concentration. In this algorithm, manual intervention was introduced into the GEP algorithm to improve the algorithm evolution speed and the quality of the solution. DSCE-GEP algorithm is a simulation of human evolution. It not only has strong ability of model learning, but also can get explicit function expression of the model. In this paper, the daily PM2.5 concentration prediction in Xi 'an area is taken as an example, and the DSCE-GEP algorithm is compared with the traditional gene expression programming algorithm (GEP), the classification regression tree and extreme learning machine combination model (CART-EELM) in literature, and the convolutional neural network and long and short-term memory neural network combination model (CNN-LSTM). The experimental results show that the DSCE-GEP algorithm has higher fitting degree and is a competitive intelligent prediction algorithm.