In Mandarin Chinese system, the tone plays an important role. Different tone patterns of the same syllable may result in different meanings. People whose native language aren’t Mandarin can be distinguished by their tone patterns. Therefore, we propose a method for tone classification. First, we convert the audio signal into the spectrogram. We treat the spectrogram as images, apply them as the image inputs for image recognition convolutional neural networks, and create tone classification models. We compare different image recognition models for tone classification. This approach can achieve good accuracy without too many processes on the audio signal. The tone classification architecture can be applied to Chinese teaching methods which will lead to educational success.