Membrane based cells are the fundamental structure and function units of organisms, while evidences were increasing that liquid-liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation, but these studies lacked an effective tool to identify the sequence segments that critical for LLPS (SCOPEs). In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the SCOPEs. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted SCOPEs enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted SCOPEs enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS related research.