The ever-rising quantity of collected data of water conservancy project together with the development of cloud computing, big data and Internet of Things poses higher demands for the storage and processing of massive, multi-source and heterogeneous data that traditional theories and methods could not meet. In this research, a big data platform for grouting data of water conservancy project is designed based on B/S service mode for display, operation, and management. The functional modules of the platform mainly include data resource downloading, data set uploading and running, customized algorithms, as well as visualization of running status and results and big data.Moreover, the platform was applied for demonstration with Baihetan water conservancy project as a case study. A model for predicting the grouting injection amount per unit based on random forest together with a model of anomaly detection of grouting result based on K-Means clustering was built.By integrating structural and unstructured data and by adopting Hadoop distributed cluster and parallelized data mining algorithm, the platform could achieve integrated sharing of data resource, effective processing, knowledge discovery of data information, and improves the efficiency and accuracy of data storage and processing. This research offers a new thinking for the big data storage and computing of water conservancy project