Vulnerabilities have become one of the most serious threats to software. In order to mitigate the impact of software vulnerabilities, researchers have continuously proposed vulnerability detection approaches. Although these investigations have achieved significant success, there is still room for improvement. Traditional approaches rely on code sequences or code graphs to extract the general characteristics of code, containing excessive information that is irrelevant to vulnerabilities. Meanwhile, traditional single-model approaches are hard to handle the multiangle vulnerability information, lacking the ability to effectively detect vulnerabilities. To address the above two problems, we proposed a new vulnerability detection approach, i.e., VDLS. It first selects the execution paths with the related entities of vulnerabilities from the constructed Control Flow Graph (CFG) of the source code. Then, it combines the code sequence and the execution paths as the intermediate representation, which can capture the features of the source code from the perspectives of structures and semantics. Next, we employ a dual model (TextCNN and Transformer) to learn the local and global features based on the intermediate representation. Finally, we design a fusion method to separately fuse the weights of local and global features, aiming to achieve more accurate vulnerability detection results. To evaluate VDLS, we conducted experiments on two widely used public datasets, including FFMPeg+Qemu and Reveal. The experimental results show that VDLS achieves $0.76% \sim 15.97 %$, $3.07% \sim 53.61 %$ improvement on the FFMPeg + Qemu dataset and $0.61 % \sim 8.46 %$, $2.55 % \sim 39.13 %$ improvement on the Reveal dataset compared to eleven state-of-the-art vulnerability detection approaches in terms of accuracy and F1 score, respectively.