To strengthen the management and control of the source of risk for road transport of hazardous materials, this paper takes the trajectory data of hazardous materials transport vehicles as the analysis object, and studies the problem of optimal selection of road transport routes which is safe, economical and in line with the preference of enterprises, a context-aware, preference-based personalized route recommendation method for road transport of hazardous materials is proposed. Firstly, the historical trajectory data of hazardous materials transport vehicles is processed, and the route preferences of enterprises are learned by extracting route safe and economical features. On this basis, considering the distance and direction similarity between preference vectors, an improved K-means clustering algorithm (DDM-K-means) is proposed to obtain the categories of route preference. Secondly, according to the time, weather, and distance of the transportation tasks, the route context vectors are established. Rock clustering algorithm is used to classify the categories of route context, combined with the categories of route preference to form the categories of route. Finally, based on neural collaborative filtering, an optimal route selection algorithm (NCF-ORS) is proposed, and the preference ranking of hazardous materials road transport enterprises for route categories is obtained to recommend the optimal route for enterprises. Comparing our method with the baseline algorithms, the results showed that the personalized route recommendation method proposed in this paper had a lower mean absolute percentage error. Therefore, the research in this paper is helpful to mine more potential information from vehicle′s trajectory data, with stronger personalized route recommendation capabilities, and can provide decision support for route selection of hazardous materials road transport enterprises.