Features from different levels should be extracted from images for more accurate image classification. Deep learning is used more and more in large scale image classification. This paper proposes a deep learning framework based on deep convolutional neural network that can be applied for the large scale image classification. The proposed framework has modified the framework and the internal structure of the classical deep convolutional neural network AlexNet to improve the feature representation ability of the network. Furthermore, this framework has the ability of learning image features and binary hash simultaneously by introducing the hidden layer in the full-connection layer. The proposal has been validated in showing significance improvement through the serial experiments in three commonly used databases. Lastly, different effects of different optimization methods are analyzed.