Crowd path planning aims to find the optimal path between the source and destination for multiple agents in crowd scenes. The advent of parallel theory and digital twin technologies provides a novel platform for simulating crowd path planning, which has become increasingly popular in various applications, such as pedestrian evacuation, intelligent transportation, and civil planning. The widely used strategy for crowd path planning emphasizes the objective factors, such as user-specific guidance, shortest path and crowd density. However, this strategy ignores the subjective emotion of agents, which can have significant impact on the diverse path choices of each agent. To tackle this challenge, we present a novel Emotion Contagion Model (ECM) to dynamically conduct path planning in crowded environments by incorporating the emotion of each agent. The proposed method provides a solution to the long-standing high-level affective issue for virtual agents during path search. Firstly, to bridge the gap between emotion states and path choices, the emotion preference is defined based on personality traits of multiple agents. Secondly, an emotion contagion algorithm is proposed to recognize the collective patterns of these agents, which can reveal the dynamical variation of emotion preference under crowded complex environments. Finally, to solve the emotion-to-path mapping, we propose a leastexpected-time objective function to find the optimal path choice for each agent according to the navigation graph in the given scenario. Experimental results on various scenarios, including the subway station, railway station square, fire evacuation and indoor environment, verify the effectiveness of the ECM compared with the state-of-the-art methods.