The massive scale and diverse types of heterogeneous data lead to increased resource consumption and difficulty in feature extraction in data processing. However, large convolutions may not be able to adapt well to the feature extraction requirements of different types of data. Small convolution kernels can enhance feature extraction capabilities and better handle various features in heterogeneous data, thereby improving the classification performance of the entire algorithm for massive heterogeneous data. Therefore, this study proposes a multi label classification algorithm for massive heterogeneous data based on deep convolutional neural networks. The algorithm constructs an improved deep convolution neural network framework, uses convolution layers to extract features of heterogeneous data, and uses the idea of resolving large convolution integrals into small convolutions to reduce the risk of over fitting. In the pooling layer, a hybrid pooling method of adaptive threshold is used to reduce the dimension of heterogeneous data features extracted from the convolution layer. The dimension reduction results are taken as the input of the full connection layer, and the multi label heterogeneous data is classified by softmax classifier. In addition, the central loss function is used to constrain the loss function of softmax to enhance the multi label classification capability of the network. The experimental results show that when the size of convolution kernel is 5*5 and the number is 9, the proposed method achieves the best performance and the lowest classification loss rate.