Networks of large scales are an essential component in supercomputing systems as well as in data centers. As the network scale increases, the probability of processor/server failures also inevitably increases. It is therefore a worthwhile undertaking to make efforts reducing, as much as possible, the effect of faulty processors/servers to the entire network. This paper introduces a new class of network architectures, called circulant-based recursive networks (CRNs), and investigates CRN’s diameter, connectivity, and in particular, the fault diagnosability under the two diagnostic models−the PMC and the comparison diagnostic models. CRNs are a generalization of some well-known interconnection networks−hypercube, k-ary n-cube network and the data center network BCube, as well as some other less-known networks. In addition to obtaining its diagnosability properties, the paper also presents a one-to-one (unicast) path construction algorithm named SPath. Based on SPath, we further propose an algorithm FTPath for CRNs finding a fault-tolerant path between any two vertices, provided that the number of faulty vertices is no more than its connectivity minus one. Three parameters−average distance, message density, and cost−are used to assess CRNs’ performance. Experimental comparisons are conducted, and the results indicate that the average path length obtained by the algorithm SPath (resp., FTPath) is shorter than that of the Depth-First Search algorithm (DFS) and is on a par with the Breath-First Search algorithm (BFS).