Abstract: To meet the demand of soil moisture content monitoring on a national scale, at the level of data acquisition, a soil moisture content data acquisition and perception technology system based on in-situ monitoring of automatic soil moisture content monitoring station and multi-source heterogeneous thematic data was constructed in this study, which realized the online monitoring of soil moisture content and multi-source data fusion. Further in terms of data quality control in the soil moisture data quality control strategy was proposed for data cleaning and established the soil moisture content data correction and interpolation model, in the cloud background received by the TCP/IP protocol of the Internet of things device came back after the packet data parsing and quality judgment. For abnormal or missing data, through the calibration data interpolation model to predict, avoided the interruption problem caused by the missing data, ensured data accuracy, integrity, and availability. Moreover, the soil moisture content data correction and interpolation model adopted the deep learning algorithm and the Stacking strategy to merge the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) networks. The soil moisture content monitoring system facing the national scale had the characteristics of a large number of automatic station devices, massive user visits, and vast amounts of data computation, and had the characteristics of high frequency, high concurrency, and continuous growth. The ordinary web architecture could not ensure the stable and reliable operation of the system. Therefore, the system adopted the cloud-native technology system suitable for the cloud computing characteristics, used the micro-service architecture and the container technology to construct a flexible development model, and improved the efficiency of computing resource utilization. The system architecture design was based on the cloud-native technology, the module of the system was flexibly developed and deployed in the form of micro-services, the independent instance of packaging and running container technology was used to solve the problem of environmental configuration and resource utilization efficiency, and the container was dynamically scheduled to optimize the utilization of cloud computing resources. The core modules such as soil moisture content data reporting collection, soil moisture content data visualization analysis, and soil moisture content data mining application were arranged in the system. Based on GIS (Geographic Information System) spatial analysis and WebGL technology, the front-end 3D WebGIS data analysis function module was developed, and the collaborative Kriging interpolation method was used to realize the online analysis and visual mapping of collaborative soil moisture content, land use types, altitude, and other multi-source data. The system mined the data value deeply and utilized the deep learning algorithm to realize the soil moisture content prediction service which used the data of the past 8 days to predict the data of the next day. Based on the principle of water balance, the application service of irrigation decision was realized. By selecting the crop coefficient recommended by FAO and the growth stage of the corresponding planting crops, the water demand of crops was calculated, and the water balance analysis was realized and the reference irrigation water quantity was recommended. Since its application, the system had been deeply applied in more than 21 provinces, 970 automatic monitoring stations had been established, and more than 60 million automatic moisture monitoring stations had been collected. The system provided reliable data sources and technical support for decision-making departments, agricultural technicians, researchers, and other users to master the current situation of soil moisture content, guide agricultural water-saving irrigation, and obtain accurate and continuous soil moisture content scientific research data.