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With the development of rice phenomics research, it is of great significance for comprehensively analyzing, mining and applying the rice phenomics data. In order to integrate the knowledge related to rice phenomics and explore the factors affecting rice phenotypic traits，the rice phenomics knowledge graph system was implemented. Rice phenomics knowledge graph system consisted of functional modules such as entity recognition, entity query, relational query and knowledge visualization. The rice phenomics data were downloaded by a distributed data website crawler from the National Rice Data Center website, and the interactive encyclopedia website was taken as auxiliary data sources to obtain rice phenomics dataset. The dataset was preprocessed with TF-IDF and latent semantic indexing method and classified and labeling manually firstly, and then machine learning approaches were applied for training and testing. The rice phenomics entity classification was studied based on stacking ensemble learning integrated with basic learning classifier, such as K-nearest neighbor, support vector machine, random forests and gradient boosting decision tree. Based on stacking ensemble learning classifier, different types of rice phenomics data showed fine ability for entity classification. For the unbalanced rice phenomics entities, comparing with the support vector machine algorithm, the K-nearest neighbor algorithm, the random forest algorithm and the gradient boosting decision tree algorithm, the proposed method showed the best performance, i.e. the F1-Measure of Gene entities can reach 90.47%. The overall accuracy was 80.55%, and it was 6.78 percentage points higher than those of the other four basic classifiers.