Due to the diverse product descriptions, dense technical terms, and significant contextual differences in cross-border e-commerce, machine translation is prone to issues such as word ambiguity, grammatical incoherence, and pragmatic deviations, which affect the transmission of product information and the reusability of the data. This paper focuses on error identification, corpus correction, and resource construction, establishing a systematic vocabulary and terminology calibration system, improving the corpus processing procedures, correcting syntactic and semantic transformation rules, addressing target language environmental factors, and then conducting the collection, cleaning, comparison, and annotation of product description information. On this basis, a dataset consisting of the original text, machine translation, error types, corrected text, and quality grades is constructed, forming a high-level cross-border e-commerce product introduction corpus that can be retrieved, tracked, and dynamically maintained. The aim is to improve the accuracy and readability of the translations and to support the generation of product content, post-editing, and the use of multiple languages.