[Objective] This paper develops a new model to improve the perception of structural features and correlation between text features, aiming to fully explore the internal semantics and extract attributes. [Methods] First, we extracted the features of text, syntax and part of speech. Then, we merged different features to obtain complete text structure features. Third, we designed a multi-layer interactive attention mechanism, which focuses on the deep correlation between text structural features and text features. Fourth, we adopted bilinear fusion strategy to ensure the information integrity. Finally, we extracted attributes with common classifiers. [Results] We examined the new model with publicly available data sets, and found its extraction accuracy was at least 1.2 percentage point higher than that of the existing methods. [Limitations] The model was insensitive to implicit attribute words, and the performance of the model will be greatly reduced with more than three implicit attribute words in the sentence. [Conclusions] The proposed method can effectively improve the accuracy of commodity attributes extraction.
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