Today, many species of birds are rarely found, and it is difficult to classify bird species when found. For example, for different scenarios, birds come with different sizes, forms, colors and from a human viewpoint with different angles. Indeed, the images show different differences that need to be recorded as audio recognition of bird species. It is also easier for people to identify birds in the pictures. Today, using deep convolutional neural network (DCNN) on GoogLeNet framework bird species classification is possible. For this experiment, a bird image was converted into a gray scale format that generated the autograph. After examining each and every autograph that calculates the score sheet from each node and predicts the respective bird species after the score sheet analysis. In this experiment, the Caltech-UCSD Birds 200 [CUB-200-2011] data collection was used for both training and testing purposes. For training purpose 500 labeled data are used and 200 unlabeled data are used for testing. For classification, Deep Convolutional Neural Networks are used and parallel processing was carried out using GPU technology. Final results show that the DCNN algorithm can be predicted at 88.33% of bird species. The experimental research is performed on the linux operating systems with Tensor flow library and using a NVIDIA Geforce GTX 680 with 2 GB RAM.