A network equipment early warning prototype system comprises a machine room data acquisition device, an API interface I/O, a database memory, a time sequence fault prediction model and a fault prediction GUI interface which are electrically connected in sequence. The function of the time sequence fault prediction model is that a current DBN model based on an RBM algorithm is realized through a python language, and historical data is called for model training; for the trained model, the system outputs time series fault early warning data in a future period of time according to the real-time data of the existing network machine room; the function of the fault prediction GUI interface is as follows: through a system GUI interface developed by QT software, early warning data realized by background model operation is extracted from the whole interface, the time sequence early warning condition of each device is displayed in the form of a trend curve, and red light display is carried out ondevices with high-risk faults recently. The network core router management and control method is applied to network core router management and control in the smart campus operation and maintenance process.