Changing lanes according to the lane change decision rules is one of the common decision-making methods for unmanned vehicles. In view of the difficulty in extracting decision rules and the less research in the dense fog environment, this paper studies the decision-making behavior of lane change in dense fog environment of expressway. First, 24 professional drivers were recruited for the experiment, and the AutoSim driving simulator was used to build a virtual highway dense fog environment for driving experiments; Second, a method for extracting lane change decision rules based on CART decision tree was proposed, and 15 lane change decision rules were extracted from it; Final, the lane change decision rules were verified. The results show that it is feasible to use the CART decision tree algorithm to extract the lane change decision rules in the dense fog environment of the expressway; The extracted rules can accurately reflect the decision-making process of the driver's lane change behavior, which can provide a certain theoretical support for the lane-changing decision-making of unmanned vehicles in dense fog environment of expressway.