Deep learning compilers have been widely employed with the rapid development of deep learning models and hardware architectures. At present, the compilation optimization and tuning methods of deep learning models mainly rely on high-performance operator libraries and automatic compiler tuning. However, facing various target operators and adaptation requirements of several hardware platforms, high-performance operator libraries should conduct multiple implementations for different architectures. Additionally, existing auto-tuning schemes face challenges in substantial search overheads and interpretability. To this end, this study proposes AutoConfig, an automatic configuration mechanism for deep learning compilation optimization. Targeting different deep learning workloads and multiple hardware platforms, AutoConfig builds interpretable performance analysis models, conducts a thorough assessment via static information extraction and dynamic overhead measurement, and automates algorithm selection and configuration tuning for code generation. The key innovation of this study is combining the optimization analysis model and a configurable code generation strategy, which ensures a performance acceleration effect and reduces repeated development overheads with the simplified tuning process. Furthermore, this study integrates AutoConfig into a deep learning compiler Buddy Compiler, builds analysis models for convolution and matrix multiplication optimization, and evaluates the optimization on multiple SIMD hardware platforms. Experimental results indicate that AutoConfig effectively completes parameter configuration and algorithm selection in the code generation strategy. Additionally, compared with the codes by manual or automatic optimization, the codes generated by AutoConfig can yield comparable performance without both the repeated manual tuning implementation overheads and auto-tuning search overheads.