Abstract: Abstract: The diseases and insect pests of greenhouse winter jujube are one of the main factors that restrict the yield and quality of winter jujube. The timely prediction of the jujube diseases and insect pests is the prerequisite to prevent and control diseases and insect pests. It is difficult to establish an accurate forecasting model of diseases and insect pests using traditional mathematical method and neural network (NN) because of many complex factors that lead to the occurrence of diseases and insect pests of winter jujube, including the meteorological conditions (such as temperature, sunlight, humidity), soil conditions (such as moisture, soil heavy metals), and biological characteristics (such as roots, leaves). During the process of forecasting model training, due to the defects of artificial design features and the unpredictable complexity in the design process, the accuracy of disease and insect pest prediction and the efficiency of the design features can't have a lot of space of ascension. It is possible to automatically forecast diseases and insect pests of winter jujube with the development of agricultural IOT (Internet of Things), smart camera equipment, high performance and large capacity data storage, computer and network technology as well as the massive complex data processing technology. Faced with the problem of complexity and uncertainty of diseases and insect pests prediction of winter jujube, a forecasting model of winter jujube diseases and insect pests is proposed based on the modified deep belief network (DBN). Due to the merits of the DBN, the prediction model of disease and insect pest based on modified DBN can not only utilize 20 kinds of environmental information data, but also introduce the similarity between the prior information and the constraints of the current information. The modified DBN consists of a visible input layer, several hidden layers, and an output layer. The visible layer inputs the data, whose range has been normalized into 0,1; the hidden layers are invisible, in which binary values are used, and activated by the sigmoid kernel function. Via simulating neural connecting structure of human brain and introducing the supervised information by restricting the similarity between feature vectors in the learning process, the proposed model can automatically learn senior nonlinear hierarchical combination features from the environmental information of winter jujube growth, which is suitable for data classification and importing high-level features into traditional BP (back propagation) neural network classifier to improve the disease forecasting precision. The disease and insect pest prediction is conducted by BP network in the top level of DBN. Experiments on the actual database of disease and insect pest of greenhouse winter jujube are performed. After a large number of training samples and training times, the prediction accuracy rate of diseases and insect pests is greatly improved. The accuracy rate of forecasting result is over than 84%. The experimental results show that the proposed model has provided a technical basis and support for the automatic crop disease forecasting with environmental information obtained in fields, and has great application prospect in disease and insect pest prediction of greenhouse winter jujube. As there are many factors affecting crop diseases, practically, some factors vary with the time, how to use the environmental information of crop growth to build a powerful and practical crop disease forecasting method still needs further study.