With the continuous development of electric vehicle (EV) technology, there is an increasing need to analyze the factors influencing customers’ purchase intentions. According to the data of customers’ vehicle experience evaluation and personal information, this paper develops the analysis models of influencing factors using the analysis of variance algorithm (ANOVA) and Kruskal–Wallis algorithm. Then, the purchase intention model for EVs is proposed using the random forest method. Finally, the optimization model for the EV sales plan was built. The results show that the main factors influencing customers’ purchases are different for different vehicle brands. However, the customer’s evaluation of the vehicle experience has a greater influence on the customer’s purchase. Compared to other prediction models, the random forest model has the highest accuracy. For 3 EV brands, the prediction accuracies are 97.8%, 98.9%, and 97.6%. In addition, this paper predicts the purchase intentions of 15 customers. By optimizing the sales plans for 3 EV brands, the predicted purchase rate of 15 customers increased from 40% to 53%. The research work contributes to the sales of electric vehicles, the accurate positioning of customers, and the identification of more potential customers.

With the booming development of door-to-door housekeeping service, the platform faces the problem of order assignment. Improving the matching mechanism between orders and housekeepers based on a dynamic programming (DP) algorithm can not only achieve flexible order allocation but can also improve the service efficiency and service quality. In this paper, a single objective nonlinear programming model is established, which takes the maximum total weight value as the objective function to study the order allocation problem under offline and online conditions. Under the offline condition, the number of housekeepers is taken as the decision variable. The status of order and housekeeper, order time, and action trajectory are taken as constraints. For online assignment, the order backlog status is treated as the decision variable. The reliability of the model was verified using real data from 20 groups of housekeepers and 50 groups of orders. Finally, the effect of order backlog on online allocation is discussed and the optimal threshold and maximum weight are found. The online order assignment model is compared with the nearest distance assignment model. The results show that the online assignment model with a total weighted score of 1045.14 is better than the nearest distance assignment model with a score of 810.25.