This study aims to construct an efficient consumer price index (CPI) forecasting model to provide policymakers, investors, and businesses with more accurate forecasts of future price levels and inflation trends. in this study, a combined model that integrates autoregressive integrated moving average (ARIMA) with long short-term memory (LSTM) networks is introduced. The model first captures the linear trend of CPI data using the ARIMA model, and then inputs the residuals into the LSTM network to predict the nonlinear part. The model is trained and tested using monthly Chinese CPI data. The findings indicate that the ARIMA-LSTM hybrid model outshines the single ARIMA model regarding prediction accuracy, its predicted values are closely aligned with the actual values, and the model residual series passes the Q-test, which suggests that the model exhibits a strong fitting capability. The article also introduces the evaluation indexes of the model and compares the prediction performance of the single ARIMA model and the ARIMA-LSTM hybrid model. Finally, the article concludes that the ARIMA-LSTM hybrid model has high accuracy and reliability in CPI forecasting, which provides a powerful tool for forecasting future price trends.