Objective To compare the mosquito capturing effectiveness of an artifical intelligent (AI)-based mosquito surveillance method (the intelligent trap method) with three traditional vector surveillance methods, including the light trap method, the human-baited double net trap method and the BG-trap method, so as to provide insights into the application of AI-based intelligent systems in mosquito surveillance. Methods A residential community with more than 500 households and a high mosquito density was selected from Xiuzhou District, Jiaxing City. Mosquito surveillance was simultaneously performed with the light trap method, the human-baited double net trap method, and the BG-trap method one day per week during the period from May to September 2025, while the intelligent mosquito trap was run as a 24/7 service. The light trap index, mosquito biting index, mosquito attraction index and the adult mosquito density captured by intelligent traps during the same period were calculated. The mosquito-capturing effectiveness was compared between the intelligent trap method and three traditional mosquito monitoring methods with the Kruskal-Wallis H test, and the mosquito-capturing outcomes and trends in the mosquito density of these four mosquito monitoring methods, as well as the accuracy of the intelligent trap system for identification of mosquito species and genders were descriptively analyzed. Results A total of 4 620, 23, 250, and 3 564 adult mosquitoes were captured with intelligent traps, human-baited double net traps, BG-traps, and light traps during the monitoring period, including 299 (6.47% of total mosquitoes captured), 16 (69.57%), 247 (98.80%), and 88 Aedes albopictus (2.47%), respectively. At weekly monitoring days with three traditional monitoring methods, 2 female adult mosquitoes were captured with intelligent traps during daytime (15∶00-17∶00), with an average adult mosquito density of 0.05 mosquitoes per hour, and 219 female adults were captured during nighttime, with an average density of 9.95 mosquitoes per night, while a total of 21 female adults were captured with human-baited double net traps, with an average mosquito biting index of 0.95 mosquitoes per hour. In addition, a total of 150 female adults were captured with BG-traps, with an average mosquito attraction index of 6.82 mosquitoes per hour during the monitoring period, and a total of 1 104 female adults were captured with light traps, with an average light trap index of 25.09 mosquitoes per light per night. There was a significant difference in the average mosquito-capturing rate at the same period during daytime among the intelligent trap method (0.05 mosquitoes per hour), the human-baited double net trap method (0.95 mosquitoes per hour), and the BG-trap method (6.82 mosquitoes per hour) (H = 39.559, P < 0.05), with a lower average mosquito-capturing rate seen for the intelligent trap method than the human-baited double net trap method and the BG-trap method (both P < 0.05), and a lower average mosquito-capturing rate seen for the intelligent trap method than the light trap method at the same period during nighttime (9.95 mosquitoes per night vs. 25.09 mosquitoes per light per night; H = 6.377, P < 0.05). A similar trend was seen in the fluctuation of the adult mosquito density between the intelligent trap method and the light trap method, with both peaking in week 8 (June 24), at densities of 29 mosquitoes per night and 118.5 mosquitoes per light per night, respectively. In addition, the number of adult mosquitoes recorded in the backend database of the intelligent traps was higher than the manually collected count (4 620 vs. 3 339 adult mosquitoes) from May 1 to September 29, and the estimated accuracy of AI-based intelligent traps was 88.71% for identification of mosquito species and 90.03% for identification of mosquito genders. Conclusion Although the trend in fluctuation of the adult mosquito density is comparable between the intelligent trap method and the light trap method, its mosquito-capturing rate is significantly lower than those of three traditional methods, including the light trap method, human-baited double net trap method and the BG-trap method. In addition, the number of adult mosquitoes recorded in the backend database of intelligent traps differs from the manually collected count, which requires further optimizations.