This study aims to address the issues of current span-based aspect sentiment triplet extraction models, which ignore part-of-speech and syntactic knowledge and encounter conflicts in triplets. A semantic and syntactic enhanced span-based aspect sentiment triplet extraction model named SSES-SPAN is proposed. Firstly, part-of-speech and syntactic dependency knowledge is introduced into the feature encoder to enable the model to more accurately distinguish aspect and opinion terms in the text and gain a deeper understanding of their relationships. Specifically, for part-of-speech information, a weighted sum approach is employed to fuse part-of-speech contextual representation with sentence contextual representation to obtain semantic enhanced representation, aiding in the precise extraction of aspect and opinion terms. For syntactic dependency information, attention-guided graph convolution networks are used to capture syntactic dependency features and obtain syntactic dependency enhanced representation to handle complex relationships between aspect and opinion terms. Furthermore, considering the lack of a mutual exclusivity guarantee in span-level inputs, an inference strategy is employed to eliminate conflicting triplets. Extensive experiments on benchmark datasets demonstrate that the proposed model outperforms state-of-the-art methods in terms of effectiveness and robustness.