The Abstract Meaning Representation (AMR) parsing aims at capturing the meaning of a sentence in the form of an AMR graph. Sequence-to-sequence (seq2seq)-based methods, utilizing powerful Encoder-Decoder pre-trained language models (PLMs), have shown promising performance. Subsequent works have further improved the utilization of AMR graph information for seq2seq models. However, seq2seq models generate output sequence incrementally, and inaccurate subsequence at the beginning can negatively impact final outputs, also the interconnection between other linguistic representation formats and AMR remains an underexplored domain in existing research. To mitigate the issue of error propagation and to investigate the guiding influence of other representation formats on PLMs, we propose a novel approach of Linguistic Guidance for Seq2seq AMR parsing (LGSA). Our proposed LGSA incorporates the very limited information of various linguistic representation formats as guidance on the Encoder side, which can effectively enhance PLMs to their further potential, and boost AMR parsing. The results on proverbial benchmark AMR2.0 and AMR3.0 demonstrate the efficacy of LGSA, which can improve seq2seq AMR parsers without silver AMR data or alignment information. Moreover, we evaluate the generalization of LGSA by conducting experiments on out-of-domain datasets, and the results indicate that LGSA is even effective in such challenging scenarios.