Plant vacuoles are the most important organelles for plant growth, development, and defense, and they play an important role in many types of stress responses. An important function of vacuole proteins is the transport of various classes of amino acids, ions, sugars, and other molecules. Accurate identification of plant vacuole proteins (PVPs) is crucial for revealing their biological functions. In this study, a new predictor named iPVP-DRLF was developed to identify PVPs specifically and effectively. The workflow is shown in the figure below.The protein sequences are first converted into feature vectors by using the pretrained embedding models (UniRep and BiLSTM) and classic sequence extraction methods (DDE and DPC). Afterward, the sequential forward search (SFS) feature selection method is applied to filter the redundant information for each individual descriptor. Finally, the four optimal feature subsets are merged to form the multiview features, the representation ability of which is further optimized by SFS. The resulting fusion features are subsequently fed into light gradient boosting machine (LGBM) classifier to make prediction. Our method shows superior results in independent set tests and practical scenarios.The involved datasets and Python scripts can also be freely downloaded at https://github.com/jiaoshihu/iAVP-DRLF. The proposed model may serve as an efficient tool to assist researchers with their experimental research.