To realize the intelligent diagnosis of bearing faults, an intelligent fault diagnosis method for the thin-wall bearing of a robot based on information fusion was studied. First, a test and multi-information data acquisition system of the thin-wall bearing of a robot was built by acquiring acoustic emission and vibration acceleration signals. Then, data from acoustic emission and vibration acceleration signals detected during the test of thin-wall bearing under different fault types, equivalent loads, and rotational speeds were obtained using an orthogonal experimental method. A thin-wall single-row angular contact ball bearing (ZR71820) was used as the research object, and pitting and micro-crack defects were produced on the bearing outer ring, inner ring, and rolling bod. Finally, the root mean square value and kurtosis index in the time domain, as well as the root mean square frequency in the frequency domain, were selected as the characteristic parameters of the vibration and acoustic emission signals. Fault diagnosis of thin-wall bearings based on single vibration or acoustic emission signals was conducted. In addition, an intelligent fault diagnosis of thin-wall bearings were researched based on the fusion characteristics of acoustic emission and vibration acceleration signals using Self-Organization feature Map (SOM) and Back-Propagation (BP) neural networks. Experimental results indicate that the accuracies of fault diagnoses based on vibration signals, acoustic emission signals, and BP and SOM neural network information fusion are 85.7%, 81.0%, 93.5%, and 95.2%, respectively. The accuracy of intelligent fault diagnosis based on SOM neural network information fusion of the thin-wall bearing is 9.5%, 14.2%, and 1.7% higher than that of single vibration, acoustic emission signals, and BP neural network information fusion, respectively.