The ability to forecast short-term traffic flow is crucial for enhancing the precision of real-time traffic data and expanding the advantages of driving a car. A short-term traffic flow forecast model based on CNN-LSTM-AM was suggested to facilitate the application and decision-making of intelligent transportation systems by reliably predicting the short-term traffic flow scenario in the future. Initially, the traffic sequence of traffic sequences was captured using a convolutional neural network (CNN) in order to extract the dynamic changes of the traffic flow data. Second, the long short-term memory (LSTM) network used the remarkable vector of the time sequence extraction as its input. Ultimately, the attention mechanism (AM) was used to assign various weights to the LSTM hidden layer, emphasise the significance of key attributes, and finalise the traffic flow estimate. Using pertinent data from the PEMS expressway database in California, USA. Through experiments, it was contrasted with other neural network prediction models. The CNN-LSTM-AM model's mean absolute percentage error (MAPE) value is 0.254 578%, and its R² value is 0.583 152, according to the results. Compared to earlier comparable models, the model reported has a higher prediction capacity. The approach taken can offer a conceptual model for forecasting future short-term traffic flow.