The approaches based on machine learning technology were used to source localization in adverse acoustic environment in recent years. However, the features extracted from the GCC vectors are no significant differences in different location, resulting in an increase in the misclassification. In order to mitigate this problem, this paper introduced the algorithm based on classification of novel cross-correlation function. Firstly, the cross-correlation function is calculated by joint weighting of normalized Phase Transformation and Smooth Coherence Transform (PHAT-SCOT) in each location. Then, acoustic source location is estimated by the Fisher Weighted Naive-Bayes Classifier(FWNBC), which gives different weights according to the importance of features in classification to improve the accuracy. At the same time, the new method was verified in real environment. The experiments all showed that our new method outperforms the baseline algorithm not only in simulating situation but also in real localizing system.