In recent years, China’s agricultural plant protection has become increasingly important, and the forecasting system of agricultural plant protection is not perfect. The basic reason is that the collection, mining, and decision-making ability of agricultural big data are insufficient. At present, there are many information platforms related to agricultural plant protection on the market, but they all face problems, such as insufficient classification of resources and inaccurate resource information. As a major agricultural province, the problem of plant protection in Anhui Province is particularly serious. To promote the development of a plant protection system in Anhui Province, this article uses the digital image library of agricultural plant protection as the bottom layer, and aims to realize the distributed storage and processing of data resources by constructing a platform for big data management, analysis, mining, and visual display. To meet the actual requirements of agricultural production, management decision making, and technological innovation, we conduct technical research and product research on a system framework, data cleaning, data mining, knowledge discovery, cognitive computing, and data modeling. We construct a big data platform integrating management, sharing, innovative applications, and services. We provide practitioners with accurate plant protection information, such as identification and auxiliary diagnosis of agricultural disasters, prediction of agricultural disasters, and plant protection knowledge. We overcome temporal and geographical restrictions using the Internet to help practitioners solve the difficult problems of plant protection in production in real time. This measure can reduce economic costs, reduce operation intensity, and improve the timeliness of prevention and control. Finally, we provide suggestions to address the shortcomings of current big data platforms for plant protection. In the future, it will be necessary to supplement the platform with further informational data that it lacks, such as data on remote sensing, meteorology, and soil; increase the number of diseases, pests, and weeds in the database; improve the data sharing level; optimize the data analysis technology; strengthen data application and promotion; and improve data security guarantees, become an important part of smart agriculture