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The current greenhouse data acquisition system is implemented in the way that data acquisition terminal uploads data to the host computer to manage the data or transfer them to cloud server. The network structure is relatively complex and the power consumption is large. In order to solve the above problems, a greenhouse environment monitoring and temperature prediction system was developed by using the Internet of Things, cloud services and WeChat platform. In this system, the data collection terminal directly connected the Internet to the cloud server through WiFi/GPRS to interact with the data, and the mobile terminal accessed the cloud server to obtain the data service through the WeChat public number. The temperature forecasting model adopted the differential time series model to solve the influence of seasonal periodicity in the temperature prediction process. The data analysis showed that the system effectively realized the lightweight and mobility of the data acquisition terminal. The relative error of temperature monitoring was less than 4.96%, and the relative error of temperature prediction was less than 3%. The prediction result has high precision and can meet the needs of daily production.