For indoor sound source localization algorithm based on microphone arrays, its accuracy is greatly influenced by the reverberation and noise. Traditional sound source localization approaches cannot keep high localization accuracy in strong reverberation and low signal-to-noise ratio environments. To tackle this problem, a novel indoor sound source localization algorithm based on convolutional neural network is proposed. By extracting the phase weighted generalized cross correlation function of the received signals from microphone arrays as training feature, the three-dimensional localization information of target sound source can be obtained. Experiments based on NOIZEUS database demonstrate that the proposed algorithm can be adapted to different acoustic conditions via training. Compared with other learning based indoor sound source localization algorithms, the proposed algorithm has good localization performance and robustness in strong reverberation and low signal-to-noise ratio environment, suggesting high research and application value.