Aiming at the problem of inaccurate measurement results caused by the influence of environmental factors on the line wind speed measured by the time difference method, a data fusion method based on adaptive fireworks-BP neural network ( IFWA-BP) is proposed. Data fusion of linear wind speed information and environmental information is used to reduce the inaccuracy of linear wind speed measurement through multi-source information complementation. The adaptive firework algorithm introduces adaptive inertia weights into the firework algorithm and improves the explosion operator to enhance the global search ability of the firework algorithm, thereby optimizing the optimization process of the weights and thresholds in the BP neural network. In order to compare the fusion effect of the IFWA-BP fusion model, a multi-algorithm fusion model comparison experiment was carried out. The experimental results show that the IFWA-BP fusion model reduces the error of linear wind speed measurement and makes the accuracy of the linear wind speed measurement system reach 98. 48%.