Due to the rapid development of 5G cellular network， its coverage will be increasingly better， thus cellular network localization is a very promising technical object for research. This paper is inspired by the fingerprint localization method in wireless localization. Under the premise that the time cost of data collection is similar， a high-speed， high-precision and low-occupancy localization method is accomplished by using the emerging deep learning technology instead of the heavy fingerprint library application and distance calculation in the localization process of fingerprint localization. In this method， a convolutional neural network is built， and the training set is constructed by selecting the appropriate input data format based on the amount of features， such as received signal intensity indication， phase and direction of arrival， of the 5G antenna signal. The trained convolutional neural network can replace the huge fingerprint library in fingerprint localization， which is very beneficial to achieve localization directly in 5G mobile devices. In addition， although convolutional neural networks consume a lot of time during the training process， the classification and localization performed after the training is completed with high speed， which can guarantee the real-time implementation of localization. The trained convolutional neural network in this paper takes up less than 0.5 MB of space for weights and biases， and is able to achieve a localization accuracy rate of 95% and an average localization accuracy of 0.1 m in the real-world environment.