Decompilation serves as a fundamental technique in software reverse engineering, aiming to recover high-level source code from hardware-oriented binary programs to support human understanding, analysis, and re-engineering tasks. Although this technique has been extensively studied, traditional rule-based decompilers often generate decompiled code with poor readability and limited reusability. Moreover, due to long development cycles, support for emerging instruction set architectures such as RISC-V is typically delayed in conventional decompilers. With the widespread adoption of large language models (LLMs) in automated software engineering tasks and their demonstrated effectiveness, this study proposes BinDec, a RISC-V binary decompilation approach that synergistically integrates LLM and symbolic execution. The proposed method alternates between LLM-based code generation and symbolic execution-based verification, fully exploiting the code understanding and generation capabilities of LLM to produce decompiled code that is more readable and reusable, while leveraging the analysis and verification capabilities of symbolic execution to ensure semantic correctness and reliability. The effectiveness of the proposed method is evaluated through a series of experiments. Experimental results demonstrate that BinDec achieves semantic accuracy comparable to that of traditional decompilers, while significantly improving the readability of the generated decompiled code.