The fault information of the single sensor signal is limited in the process of centrifugal blower fault recognition, and the traditional convolutional neural network (CNN) has poorer feature extraction abilities when processing multi-source high-dimensional data. To address this problem, this paper proposes a fault diagnosis method based on multi-source information fusion and adaptive deep convolutional neural network (ADCNN) for centrifugal blowers. Firstly, data-level fusion of multi-source homogeneous information of centrifugal blower is realized based on correlation variance contribution rate method, and the multi-source information fusion framework is established. Then, ADCNN is used to adaptively extract the features of heterogeneous information and complete feature fusion, an ADCNN fault diagnosis model that integrates multi-source information is constructed. Finally, the proposed method is applied to the fault diagnosis of centrifugal blower rotor and compared with the traditional fusion method, CNN, BPNN, SVM. The experiment results show that the proposed method has superior diagnosis accuracy and robustness than other methods.