In order to improve the accuracy of WiFi-based indoor positioning and reduce the calculating time, this study proposes an indoor location algorithm combining Convolutional Neural Networks (CNN) with traditional fingerprint library. Based on the Received Signal Strength Indication (RSSI) data, the algorithm first uses the CNN model to predict the initial position of the measured point according to the real-time input data. Under the premise that the large-scale prediction position is guaranteed to be correct, the fingerprint points in the traditional fingerprint database are combined to determine the final prediction position with higher accuracy. The results show that the location accuracy of the error within 1 m is about 65%, the location accuracy of the error within 1.5 m is about 85%, and the error is stable under the premise that the timeliness is required.