When college students learn words, they are usually controlled by their teachers and school administrators. However, there are still challenges in word learning, such as the lack of visual memory for pictures, incomplete word meanings, and limited interaction between users. In this study, a new network structure called the stepped image semantic segmentation fusion network is proposed to address these issues. This network combines different modalities to improve the motivation and effectiveness of word learning. By optimizing the spatial pooling pyramid module and using a new activation function called Funnel ReLU (FReLU), the network achieves higher accuracy in image segmentation tasks. The improved network also reduces errors in predicting boundaries of each class. Experimental results on the Englishhnd dataset show that the network achieves an impressive 96.35% accuracy for English characters. This research paves the way for further exploration into enhancing vocabulary detection in English learning through visual modal memory and advanced network techniques.

College students learn words always under both teachers’ and school administrators’ control. Based on multi-modal discourse analysis theory, the analysis of English words under the synergy of different modalities, students improve the motivation and effectiveness of word learning, but there are still some problems, such as the lack of visual modal memory of pictures, incomplete word meanings, little interaction between users, and lack of resource expansion function. To this end, this paper proposes a stepped image semantic segmentation network structure based on multi-scale feature fusion and boundary optimization. The network aims at improving the accuracy of the network model, optimizing the spatial pooling pyramid module in Deeplab V3+ network, using a new activation function Funnel ReLU (FReLU) for vision tasks to replace the original non-linear activation function to obtain accuracy compensation, improving the overall image segmentation accuracy through accurate prediction of the boundaries of each class, reducing the intra-class error in the prediction results. The accuracy compensation is obtained by replacing the original linear activation function with FReLU. Experimental results on the Englishhnd dataset demonstrate that the improved network can achieve 96.35% accuracy for English characters with the same network parameters, training data and test data.

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