To describe the lane-changing decision mechanism of vehicles in the bottleneck section of weaving area and provide a lane-changing decision model in the emergency environment, this paper constructed a collaborative lane-changing decision model of vehicles facing bottleneck section based on the micro-trajectory information of vehicles and the human traffic flow model of social force. This model can provide a lane-changing decision method for the sudden bottleneck environment of intelligent network connection. Firstly, based on the characteristics of lane-changing decision of vehicles in the sudden bottleneck section, the vehicle equivalent mass model was constructed to improve the social force model by considering the types of vehicles and drivers. On this basis, the factors driving vehicle lane-changing were described as automatic driving force, repulsive force among vehicles and repulsive force of obstacles, and the collaborative lane-changing decision model was constructed. Then, 832 microscopic trajectory data of lane change decisions were selected and divided into calibration set and verification set. The model was calibrated using genetic algorithm with acceleration as index and Manhattan distance as objective function. The validity of the calibration method was verified based on simulated data and measured data. Finally, this model was compared with the active lane changing decision model in lane changing direction identification, lane changing intention intensity and model prediction error. The results show that the success rate of lane change direction recognition of the proposed model is 92.6%, the output lane change intention intensity is basically consistent with the measured data, and the predicted RMSPE value decreases by 0.825 on average and RE value decreases by 1.379 on average, which are significantly better than those of the active lane change decision model. The research results can provide a theoretical basis for the identification of vehicle lane change intention in the bottleneck section of intelligent network environment and traffic management and control under emergencies.