The invention discloses a turbine set online fault early warning method based on abnormality searching and combination forecasting, and belongs to the technical field of electric system early warning. The turbine set online fault early warning method includes the steps of carrying out input initializing processing responsible for segmenting an input parameter time sequence in a standardization mode, and extracting a sequence characteristic mode; carrying out abnormality characteristic boundary training: obtaining an abnormality searching reference standard by training normal state parameters; carrying out abnormality searching: determining an abnormality sequence set by searching characteristic boundary crossing; identifying an abnormality change trend through regression analysis to obtain abnormality analysis of an abnormality distribution change rule; building a forecasting model to carry out trend forecasting on abnormal changes; carrying out early warning output according to the forecasting result in cooperation with the corresponding relation between abnormality parameters and fault symptoms. According to the turbine set online fault early warning method, the defect that in traditional monitoring analysis, only a limiting value theory is used, the abnormality can not be completely identified is overcome, the abnormality early warning accuracy and the abnormality early warning depth are improved, and beneficial evidences are provided for unit fault causes and responsibility ascription.