Layer-wise Relevance Propagation (LRP) methods are widely used in the explanation of deep neural networks (DNN), especially in computer vision field for interpreting the prediction results of convolutional neural networks (CNN). Multiple LRP variations utilize a set of relevance backpropagation rules with various parameters. Moreover, composite LRPs apply different rules on segments of CNN layers. These features impose great challenge for users to design, explore, and find suitable LRP models. We develop a visual model designer, named as VisLRP, which helps LRP designers and students efficiently perform these tasks. Various LRPs are unified into an integrated framework with an intuitive workflow of parameter setup. Therefore, VisLRP allows users to interactively configure LRP models, change parameters, and then study the relevance information. Moreover, VisLRP facilitates relevance based visual analysis with two important functions: relevance-based pixel flipping and neuron ablation. Several use cases are shown to illustrate the benefits of VisLRP. User evaluation is performed to assess the usability and limitation of the visual designer