目的 比较使用1种蚊虫人工智能（AI）监测方法（智能捕捉器法）开展蚊媒监测与诱蚊灯法、双层叠帐法、BG-trap诱捕器法等3种传统蚊媒监测方法的捕蚊效果，为蚊虫AI监测的探索提供科学依据。方法 2025年5—9月，选择嘉兴市秀洲区一个总户数超过500户且蚊密度较高的小区，每周选择1 d同时使用诱蚊灯法（19∶00-次日7∶00）、双层叠帐法（15∶00-17∶00）、BG-trap诱捕器法（15∶00-17∶00）开展蚊媒监测，智能捕捉器法全天候进行监测，分别计算灯诱指数、叮咬指数、诱蚊指数及同时段智能捕捉器的成蚊密度。采用Kruskal-Wallis H检验对智能捕捉器法与3种传统蚊媒监测方法的捕蚊情况进行统计学分析，并分析4种监测方法的成蚊密度变化趋势，计算智能捕捉器法的鉴定准确率。结果 监测期间，智能捕捉器法、双层叠帐法、BG-trap诱捕器法和诱蚊灯法分别捕获成蚊4 620只、23只、250只和3 564只，其中白纹伊蚊捕获数分别为299只、16只、247只和88只，分别占各自捕蚊总数的6.47%、69.57%、98.80%和2.47%。在开展3种传统蚊媒监测的监测日，智能捕捉器法白天同时段（15∶00-17∶00）共捕获雌性成蚊2只，平均成蚊密度为0.05只/h，夜间同时段（19∶00-次日7∶00）共捕获雌性成蚊219只，平均成蚊密度为9.95只/夜；双层叠帐法共捕获雌性成蚊21只，平均叮咬指数为0.95只/h；BG-trap诱捕器法共捕获雌性成蚊150只，平均诱蚊指数为6.82只/h；诱蚊灯法共捕获雌性成蚊1 104只，平均灯诱指数为25.09只/（灯·夜）。智能捕捉器法、双层叠帐法和BG-trap诱捕器法在白天同时段的成蚊平均捕获率（0.05只/h、0.95只/h、6.82只/h）差异有统计学意义（H = 39.559，P < 0.05）；智能捕获器法的成蚊平均捕获率均低于双层叠帐法和BG-trap诱捕器法（均P < 0.05）；智能捕获器法在夜间同时段的成蚊平均捕获率（9.95只/夜）低于诱蚊灯法[25.09只/（灯·夜）]（H = 6.377，P < 0.05）。智能捕蚊器法下成蚊的密度消长趋势与诱蚊灯法相似，峰值均在6月24日（第8周），密度分别为29只/夜、118.5只/（灯·夜）。智能捕捉器5月1日至9月29日后台数据成蚊数（4 620）多于人工收集计数（3 339），蚊种鉴定准确率为88.71%（估算值），雌雄鉴定准确率为90.03%（估算值）。结论 智能捕捉器法的蚊媒消长趋势虽诱蚊灯法相近，但同时段成蚊捕获率明显低于诱蚊灯法、双层叠帐法和BG-trap诱捕器法等3种传统方法，且后台计数与人工计数有差距，需进一步优化。

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.