A Dynamical Combination of Multiple Features-Based Approach for Classifying Agricultural Text

In the traditional text classification technique, values are read as letters, which destroys the original semantic illustration of numerical attributes. Completely mining deeper latent semantics features in agricultural literature requires a novel text classification technique that makes use of multivariate characteristic dynamic fusion. The global principal semantic components of the text were extracted using the Bi-Convolutional Neural Networks (CNNs) system using an attention mechanism, and the local semantic information was extracted using the Multiple Window Convolutional Neural Network. When classifying textual content based on phenotypic quantitative categories, the deep semantic portrayal of agricultural texts may be significantly enhanced by using the attention strategy to dynamically merge the obtained various semantic features.