Protein phosphorylation, catalyzed by protein kinases (PKs), is one of the most important post-translational modifications (PTMs), and involved in regulating almost all of biological processes.Here, we report an updated server, Group-based Prediction System (GPS) 6.0, for prediction of PK-specific phosphorylation sites (p-sites) in eukaryotes. First, we pre-trained a general model using 490,762 non-redundant and non-homologous p-sites in 71,407 proteins. Then, transfer learning was conducted to obtain 651 PK-specific predictors at the group, family and single PK levels, using a well-curated data set of 30,312 known site-specific kinase-substrate relations (ssKSRs) in 7059 proteins. Ten types of sequence features were extracted and integrated by 3 types of machine learning algorithms, including penalized logistic regression (PLR), deep neural network (DNN), and Light Gradient Boosting Machine (LightGMB). Using a newly collected data set of 1459 ssKSRs in 651 proteins, we compared other existing tools to GPS 6.0, which exhibited a much higher accuracy on a number of well-studied PKs. Together with the evolutionary information, GPS 6.0 could hierarchically predict PK-specific p-sites for 46,402 PKs in 184 species.