In actual industrial production， Informer model will lose a large number of temporal characteristics during feature extraction due to its probabilistic sparsity mechanism. To address this shortcoming of Informer model and meet the demands for prediction speed and efficiency in industrial production， a Convolutional Neural Network （CNN）-enhanced Informer model was proposed. Short-Time Fourier Transform （STFT） was introduced to process sequence to obtain frequency-domain features of the data， thereby further reducing feature loss caused by probabilistic sparse attention mechanism and improving prediction accuracy. On public datasets， ETT （Electricity Transformer Temperature）， ECL （Electricity Consumption Load）， as well as one private dataset， comparative experiments were conducted between the proposed model and four models widely used in the industrial field， including Long Short-Term Memory （LSTM） and AutoRegressive Integrated Moving Average （ARIMA） model. Experimental results show that both Mean Squared Error （MSE） and Mean Absolute Error （MAE） of the proposed model are decreased.