在港口事故的发生时如何能保障港内人员安全疏散，成为港口规划运营的重点研究内容，而解决人群疏散问题的关键在于了解人群的运动规律。人群运动是一个复杂的系统，涉及到人群互动行为、城市空间形态和建筑环境等诸多因素。为了给人群疏散问题的研究提供准确可靠的研究依据，本文提出了一种基于深度卷积神经网络的人群运动仿真模型。为了获取神经网络训练所需的数据，本文采用CSRNet神经网络和DBSCAN算法从监控视频中提取真实人群轨迹数据。通过深度卷积神经网络的训练，对真实的人群行为模式进行深度学习，并利用训练出的深度卷积神经网络建立人群运动仿真模型。实验结果表明，该模型能准确地预测人群的运动行为，真实地模拟人群的运动轨迹，能够为应急疏散策略的制定和公共场所疏散通道的设计提供依据。

How to ensure the safe evacuation of personnel in port accidents has become a key research topic in port planning and operation. The key to solving the problem of crowd evacuation is to understand the movement patterns of the crowd. The key to solve the problem of crowd evacuation is to understand the movement law of the crowd. Crowd movement is a complex system, involving many factors such as crowd interaction behavior, urban spatial form, and architectural environ-ment. In order to provide accurate and reliable research basis for the study of crowd evacuation, this paper proposes a crowd motion simulation model based on deep convolutional neural network. In order to obtain the data required for neural net-work training, this paper uses CSRNet neural network and DBSCAN algorithm to extract real crowd trajectory data from surveillance video. Through the training of deep convolutional neural network, the real crowd behavior pattern is deeply studied, and the crowd motion simulation model is established by using the trained deep convolutional neural network. The experimental results show that the model can accurately predict the movement behavior of the crowd and truly simulate the movement trajectory of the crowd, which can provide a basis for the formulation of emergency evacuation strategies and the design of evacuation channels in public places.