Predicting pedestrian flow in open street environment presents substantial challenges due to the complex and dynamic nature of human movement. This paper proposes a novel model that integrates recurrent neural networks (RNN) with matrix factorization techniques to enhance temporal sequence prediction based on historical pedestrian flow data. Additionally, the model incorporates the topological structure of streets, making it adaptable to various urban environments and conditions. A specially designed encoder is used to effectively capture nuanced pedestrian flow information, thereby improving the training process and enhancing the model’s predictive capabilities. The implementation of a sliding window RNN framework further supports the dynamic analysis of crowd flow properties, including movement direction and anticipated pedestrian counts, by enabling real-time adaptation to fluctuating conditions. The proposed methodology is thoroughly evaluated using real-world datasets, demonstrating significant improvements over four baseline models and showing substantial promise for use in urban planning and the effective management of pedestrian traffic.