Smart contracts are computer programs running on blockchain platforms, which extend the functionality of the blockchain and enable complex applications. However, the potential security vulnerabilities of smart contracts can lead to significant financial losses. Symbolic execution-based security vulnerability detection methods offer advantages such as high accuracy and the ability to generate test cases that can reproduce vulnerabilities. Nevertheless, as the code size increases, symbolic execution faces challenges such as path explosion and excessive constraint-solving overhead. To address those issues, a novel approach for detecting smart contract security vulnerabilities through target-guided symbolic execution is proposed. First, vulnerable statements identified by static analysis tools or manually are treated as targets. The statements that depend on these target statements are analyzed, and the transaction sequence is augmented with symbolic constraints for the relevant variables. Second, the control flow graph (CFG) is constructed based on the bytecode of smart contracts, with the basic blocks containing the target statements and the dependentstatements located. The CFG is then pruned to generate guidance information. Third, path exploration in symbolic execution is optimized by reducing the number of basic blocks to be analyzed and reducing the time required for solving path constraints. With the guidance information, vulnerabilities are efficiently detected, and test cases capable of reproducing the vulnerabilities are generated. Based on this approach, a prototype tool named Smart-Target is developed. Experiments conducted on the SB Curated dataset in comparison with the symbolic execution tool, Mythril, demonstrate that Smart-Target reduces time overheads by 60.76% and 92.16% in vulnerability detection and replication scenarios, respectively. In addition, the analysis of target statementdependencies enhances vulnerability detection capability by identifying 22.02% more security vulnerabilities.