Accurate and high-spatiotemporal-resolution predictions of precipitable water vapor (PWV) play a crucial role in numerous atmospheric processes, including global navigation satellite system (GNSS) meteorological evaluation, weather forecasting, water cycle analysis, climate trend detection, and related fields. The calculation of PWV typically relies on meteorological parameters and conversion coefficients, leading to low accuracy and dependence on the availability of meteorological data. This article has formulated a high-accuracy, spatiotemporal-resolution PWV retrieval model that leverages nonmeteorological parameters through the implementation of a multilayer perceptron (MLP) neural network. The new retrieval model utilizes spatiotemporal information and the zenith tropospheric delay (ZTD) datasets from each GNSS station as training input parameters, with the numerical weather model PWV serving as the training output parameters. The MLP neural network was trained using datasets from 229 stations of the Crustal Movement Observation Network of China (CMONOC) and numerical weather model PWV data collected from 2005 to 2015. The experimental results indicate that the root mean square (rms) fitting accuracy between MLP PWV and ERA PWV is 1.36 mm. The MLP PWV model demonstrates an accuracy of approximately 1.6 mm on both temporal and spatial scales, with a spatiotemporal rms accuracy indicator of 1.92 mm. The testing accuracy of the MLP PWV, approximately 2 mm when compared with the numerical meteorological parameters model PWV, underscores the reliability of this work. The MLP PWV retrieval model, relying on nonmeteorological parameters, has demonstrated exceptional accuracy. By incorporating high-spatiotemporal ZTD, station spatial data, and temporal information, this model achieves superior spatiotemporal PWV accuracy, showcasing the effectiveness of the methodology presented in this study.