Aiming at the cycle slip problem in the data processing of the Beidou navigation and positioning system (BDS), a method for detecting and repairing cycle slips based on lifting wavelet combined with NAR dynamic neural network is proposed. Firstly, the nondifference cycle slip test quantity is constructed, and the epoch of cycle slip is detected by the lifting wavelet method. Then, the NAR dynamic neural network method, the improved BP neural network method and the traditional polynomial fitting method are used to analyze and compare the effect of different methods on cycle slip repair. Experimental simulation results show that in cycle slip detection, the lifting wavelet method can effectively detect small cycle slips of more than 0. 2 weeks; in cycle slip repair, the NAR neural network improves the fit of the improved BP neural network by about 40%, and the prediction accuracy is about 50% higher than the improved BP neural network, and more than 10% higher than the traditional polynomial fitting method. It is more suitable for the detection and repair of small cycle slips, and further improves the positioning accuracy.