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Suicide is a serious negative social phenomenon. In tliis study, we used Pytlion technology to obtain suicide deatli data from a network and applied mathematical statistical and geographic spatial analyses to study the spatial-temporal characteristics of suicide deaths and the relationship between suicide rate and economic development in China from 2000 to 2018. Following conclusions were drawn from the results. (1) The number of suicide deaths in China is on tlie rise. Within a year, the high-incidence period of suicide deaths is from May to June, whereas the low-incidence period is from February to March. Witliin a month, the lsl, 10th, and 20th days have the highest incidences of suicide deaths. Witliin a day, 77.2% of the suicide deaths occur from 06:00 to 19: 00, and 09:00 and 15:00 were tlie peak times in which suicide deaths take place. (2) A total of 90.98% of tlie suicide deatlis occur in southeast China. The suicide rate is liiglier in the southeast than in the northwest, higher in the soutli tlian in the nortli, and decreases gradually from east to west. At county level, a relatively high suicide rate is seen in regions spanning from Great Kliingan Mountains to Yunnan Guizhou Plateau, from Qinling-Dabashan Mountains to Dabie Mountains, and from the coast of nortliem Jiangsu to Hainan Island. (3) Most areas in China present a low-grade suicide rate. However, low-grade areas appeared to change to high-grade areas during tlie period 2000 - 2018. The hotspots of suicide deatlis spread from east to west, except for the Beijing - Tianjin - Tangshan area, Yangtze River Delta, and Pearl River Delta, which have always been suicide hotspots. (4) The spatial and temporal characteristics of suicide deatlis in China are closely related to economic development, and on a city scale, tlie suicide rate has a significant positive correlation witli the per capita GDP and urbanization rate. Tlie impact of economic factors on suicide rate is greater on the soutlieast coast than on the northwest inland. An important conclusion from this study is that the gap between the rich and poor is a key factor, leading to psychological imbalance and suicidal behavior in the poor; tlierefbre, only tlie new development path based on common prosperity is the road for people to reach happiness and health. In addition, in this study, we prove that network suicide data, obtained using the web-crawler teclmology (Pytlion), not only have the same consistency and credibility as sampling statistics but also have a better spatiotemporal resolution, witli a temporal resolution of one hour and spatial resolution of a county. Therefore, by analyzing tliis spatiotemporal dataset, we can scientifically extract the time differences in suicide deaths at quarterly, monthly, daily, and hourly scales and the spatial differences in suicide deatlis at regional, provincial, and county scales. In the future, network suicide data may become an important data source for suicide research, and the use of the Internet to monitor suicidal behavior may become an important method of suicide intervention. [ABSTRACT FROM AUTHOR]