The task of table evidence retrieval of Web Information aims to identify the most relevant Web tables from an extensive corpus for supporting Web information retrieval and text analytics. However, previous methods suffer from low accuracy in scenarios with limited computing resources and incapability of assessing the authenticity of statements with sufficient evidence, posing potential risks to Cybersecurity and spread of false information. To address these challenges, we first restructure three existing table fact verification datasets for evaluating the performance of evidence retrieval of Chinese Web Tabular Data, which accounts for a large proportion in the Web. And then, we propose a novel table evidence retrieval model based on Dense Passage Retriever for enhancing retrieval accuracy in scenarios with limited computational resources. Our experimental results across multiple datasets demonstrate that the proposed model achieves commendable accuracy, validating its effectiveness.