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 poor feature extraction abilities when processing multi-source high-dimensional data. To address these problems, 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 the centrifugal blower is realized based on correlation variance contribution rate method, and a multi-source information fusion framework is established. Then, ADCNN is used to adaptively extract the features of heterogeneous information and complete feature fusion, and an ADCNN fault diagnosis model that integrates multi-source information is constructed. Finally, the proposed method is applied to the fault diagnosis of the centrifugal blower rotor and compared with the traditional fusion method, as well as CNN, BPNN, and SVM. The experiment results show that the proposed method has superior diagnosis accuracy and robustness than other methods.