In this Zwork, Zwe propose an Asymmetric Kernel Convolutional Neural NetZwork (AKCNN) for Acoustic Scenes Classification (ASC). Its kernel shape is not the traditional square but asymmetric in Zwidth and height. It also uses Weight Normalization (WN) to accelerate the training process because it can early converge the training loss and accuracy. The best of all, WN can help increase the accuracy of ASC. TUT Acoustic Scenes 2016 Dataset  is used for evaluation. The result shoZws that AKCNN achieves accuracy 86.7%. If Zwe rank the score in DCASE2016 ASC Challenge, it shoZws that the system Zwould have a higher score than the 5th place.