Abstract: In recent years, with the increasing number and complexity of photoelectric measurement systems, the demand for fault diagnosis is also increasing. In the fault diagnosis of the photoelectric measurement system, the prediction of its tracking error is particularly important. In this paper, we propose a BP neural network algorithm optimized by the Cuckoo algorithm (CS-BP). The tracking error can be predicted by using the azimuth guidance, pitch guidance, azimuth encoder, pitch encoder and time data of the optoelectronic measurement system. Compared with the traditional neural network algorithm, this algorithm uses the excellent characteristics of Cuckoo to find the extreme value, and solves the problem that the neural network algorithm cannot get the optimal solution due to the improper setting of the initial threshold and weight. The experimental results show that, the number of iterations with CS-BP is 21 and 60 less than the traditional BP neural network and the BP neural network optimized by the genetic algorithm (GA-BP), respectively. The relative errors are 4.85% and 1.57% lower, respectively. Therefore, the CS-BP algorithm has a faster convergence speed and higher prediction accuracy, and it is suitable for fault diagnosis of photoelectric measurement system.