Research conclusions:(1)The framework and functional modules of HRC-MIS are presented according to the theory of design and maintenance of heavy-haul regional CWR. ( 2)By studying the type，management scope，observation cycle and record method of the data，the calculating method with correction factor for actual neutral temperature and the algorithm for track section load which is based on aggregated traffic pushback are presented.(3)By deeply discussing about the internal correlations among all kinds of data and their influence on the status of the railway，the methods for statistical comparison analysis considering both space and time are proposed.(4)A prediction algorithm for actual neutral temperature by using the General Dynamic Fuzzy Neural Network is presented. And the assistant decision methods including regular and real-time over-limit alarming，early warning and maintenance instruction are proposed. (5)The results of this paper are worth popularizing in other regional CWR on heavy-haul railway and have reference value for the research on the variation law and status evaluation of CWR.