Improving evacuation efficiency is a central concern in evacuation simulation research. Incorrect feedback information can affect the effectiveness of evacuation control in partially observable evacuation. In this paper, we introduce a framework for evacuation guidance control, emphasizing data prediction and correction to mitigate the impact of abnormal observed data. The framework is built upon force-driven Cellular Automaton (CA) models and employs a data correction module to rectify abnormal information. Guided by this framework, we implement the data correction module’s functionality using Back Propagation (BP) neural networks. We utilize historical simulation data to train the BP network, obtaining a model for correcting abnormal data. Additionally, we integrate the data correction model with density control algorithms to facilitate pedestrian flow management in abnormal evacuation scenarios. Subsequently, we conduct two comparative simulation experiments to verify the algorithm’s effectiveness. One experiment utilizes an abnormal data replacement method, while the other employs a data correction method. The results show that the method of abnormal data replacement is simple, but it cannot improve the control efficiency in abnormal evacuation scenarios. The data correction method proposed in this paper can effectively improve evacuation efficiency and alleviate the congestion at exits caused by abnormal data, which reduces evacuation efficiency. The results are expected to provide insights into improving evacuation systems’ efficiency and personnel safety. •A framework for evacuation guidance control with data prediction and correction•Integration of force-driven CA models and BP neural networks to rectify abnormal observed data•Validation of the proposed algorithm through comparative simulation experiments