As one of the core frameworks of the Hyperledger, Fabric provides users with private transaction space with its multi-channel design. In order to solve the problem of multi-channel resource load balancing based on distributed architecture, a Blockchain as a Service (BaaS) load balancing scheduling algorithm SC-channel based on NJW spectral clustering was proposed. The proposed algorithm took the number of platform sub-nodes as the basis for classifying the number of clusters. Firstly, based on channel the Jaccard coefficient between peer was used to construct the similarity matrix. Secondly, the Laplacian matrix was calculated to obtain the first k eigenvalues and eigenvectors, and the eigenvectors were unitized. Finally, the feature clustering was done using the classical weight-based k-means algorithm. The proposed algorithm was validated on the Kubernetes platform and its resource balance degree was compared with those of the NJW algorithm using the classic k-means and the default scheduling algorithm. Theoretical analysis and experimental results show that the BaaS resource load balancing scheduling algorithm based on spectral clustering can improve the balance of resource utilization and enhance the usability and reliability of the platform.