Abstract: Considering that the difficulty of free-hand sketch recognition，the low recognition accuracy and handcrafted features，we propose a new CNN ( Convolutional Neural Networks) structure DCSN ( Deeper-CNNSketch-Net) which is specifically designed to accommodate the unique characteristics of free-hand sketch． Firstly we use a larger convolution kernel in the first layer to obtain the structural information of the sketch． Then we use smaller stride in the first layer to keep the feature information． At last，we increase the network layers to deepen the network depth． In order to improve the recognition accuracy，we propose two new data enhancement methodsmall graphics reduction strategy and tail removal strategy to increase the diversity of data sets，then we use the extended data sets to train the DCSN network． The experimental results show that our model can achieve 70． 5% recognition accuracy on the largest free-hand image dataset，which has a good performance than the existing freehand sketch recognition methods．

Key words: free-hand sketch recognition, convolutional neural network, network model, data augmentation