Aiming at the problem that cooling load prediction of overall buildings for large-scale shopping malls cannot provide a reasonable control strategy on demand for various areas of the shopping mall. According to the characteristics of the cooling load in different areas in the mall, the grey correlation analysis method was used to screen the key influencing factors of cooling load in different areas of shopping malls, and a district cooling load forecasting model based on attention long-short term memory (Attention-LSTM) neural network was proposed. LSTM network fully considers the nonlinear relationship between air conditioning cooling load and related characteristic variables. Characteristic attention autonomously analyzes the relationship between historical information and input variables to extract important features. Time-series attention selects historical information at key moments in the LSTM network to improve the stability of the forecast effect