Abstract: Sparse matrix-vector multiplication (SpMV) is a core operation in numerical computations, widely used in scientific computing, engineering simulations, and machine learning. The performance optimization of SpMV is mainly constrained by irregular sparse patterns, with traditional optimizations relying on manually designed storage formats, computation strategies, and memory access patterns. Existing tensor compilers such as TACO and TVM use domain-specific languages (DSL) to generate high-performance operators, alleviating developers from the burdensome manual optimization tasks. However, the optimization support for sparse computations remains insufficient, making it challenging to adaptively optimize performance for different sparse patterns. To address these issues, we propose a sparse compiler framework called SparseMode, which generates efficient vectorized code for SpMV computations based on the matrix’s sparse pattern and adaptively adjusts optimization strategies according to the characteristics of the hardware platform. The compiler framework first designs a domain-specific language, SpMV-DSL, which efficiently and concisely expresses sparse matrices and computational operations in SpMV. Then, a sparse pattern-aware method is introduced, which dynamically selects computation strategies based on the matrix storage format and non-zero element distribution defined in SpMV-DSL. Finally, efficient parallel SpMV operator code is generated through sparse pattern analysis and scheduling optimizations, fully utilizing SIMD instructions to enhance performance. Experimental results on different hardware platforms show that SpMV operator code generated by SparseMode achieves up to a 2.44 times speedup compared with the existing TACO and TVM tensor compilers.