With the wide attention in the field of cloud computing, the cluster container orchestration management platform Kubernetes has been widely used in service scenarios such as automatic deployment and release of container application service, elastic expansion of application and rollback update, and fault detection and self-repairing. Fifth-generation reduced instruction-set computer (RISC-V) includes four technical characteristics and advantages: fine simplification, modularization, scalability, and open source, and it has attracted extensive attention from academia and industry. Based on the collaborative research of Kubernetes ecology and RISC-V ecology, this study supports scheduling tasks with heterogeneous instruction set architecture (ISA) for the Kubernetes scheduler. Through the quantitative analysis of various computing task requirements of RISC-V instruction set architecture in the production environment, it is found that the existing Kubernetes cannot schedule the computing tasks of RISC-V instruction set architecture, and in particular, it fails to employ the extended instruction set architecture characteristics defined by RISC-V developers to provide high-performance and reliable services. In order to solve these problems, this study proposes an ISAMatch model which comprehensively considers the affinity of the instruction set, the number of nodes in the same instruction set architecture, and the utilization of node resources, so as to realize the optimal allocation of tasks. Based on the existing cluster scheduler, this study improves its scheduling requirements for multiple instruction set architecture tasks. In addition, compared with the default scheduler whose accuracy rate is 62% (scheduling RISC-V basic instruction set tasks), 41% (scheduling RISC-V extended instruction set tasks), and 67% (scheduling RISC-V instruction set tasks with “RISC-V” node matching label), ISAMatch model can achieve a task scheduling accuracy of 100% without considering resource constraints.