This paper deeply explores dependency parsing, a key task in natural language processing. Through experiments with multiple dependency parsers, it elaborates on the acquisition methods of experimental corpora, the specific methods adopted in the experiments, and conducts detailed data statistics and in-depth analysis of the experimental results. Dependency parsing aims to reveal the dependency relationships between words in a sentence and plays a crucial role in understanding sentence structure and semantics. This paper carefully selects multiple public corpora, including Penn Treebank, CoNLL-2009, and OntoNotes, and conducts strict preprocessing and reasonable division of the corpora. The experiments adopt three methods: single parser experiment, comparison experiment, and cross-domain experiment, and use indicators such as accuracy, recall, and F1 value to evaluate the performance of different parsers. The experimental results show that the deep learning-based parser spaCy performs excellently in overall performance but also requires a large amount of computing resources and time. At the same time, this paper also analyzes the performance differences and influencing factors of different parsers in processing long sentences, complex structure sentences, and sentences with special language phenomena as well as in corpora from different fields.