Deep neural networks have been widely used in fields such as autonomous driving and smart healthcare. Like traditional software, deep neural networks inevitably contain defects, and it may cause serious consequences if they make wrong decisions. Therefore, the quality assurance of deep neural networks has received extensive attention. However, deep neural networks are quite different from traditional software. Traditional software quality assurance methods cannot be directly applied to deep neural networks, and targeted quality assurance methods need to be designed. Software fault localization is one of the important methods to ensure software quality. The spectrum-based fault localization method has achieved good results in traditional software fault localization methods, but it cannot be directly applied to deep neural networks. In this study, based on the traditional software fault localization methods, a spectrum-based fault localization approach named Deep-SBFL for deep neural network is proposed. The approach firstly collects the neuron output information and the prediction results of deep neural network as the spectrum. The spectrum is then further calculated as the contribution information, which can be used to quantify the contribution of neurons to the predicted results. Finally, a suspicious formula for the defect localization of deep neural network is proposed. Based on the contribution information, the suspiciousness scores of neurons in deep neural network are calculated and ranked to find out the most likely defective neurons. To verify the effectiveness of the method, E_Inspect@n (the number of defects successfully located by inpecting the first n positions of the sorted list) and EXAM (the percentage of elements that must be checked before finding defect elements) were evaluated on a deep neural network trained by the MNIST data set. Experimental results show that this approach can effectively locate different types of defects in deep neural networks.