<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Using the CNN network model and attention mechanism, this paper completes the construction of the cognitive graph pattern layer based on Protégé programming, crawls the relevant data from the dimensions of language ability, learning ability, thinking, and cultural character of multicultural literacy in English teaching, extracts the entity-relationship of multicultural literacy in English teaching, stores it in the Neo4j corpus, and ultimately completes the construction of the cognitive graph for the cultivation of multicultural literacy in English teaching. Following simulated experiments to evaluate the algorithm’s performance, a case study was carried out to foster English multicultural literacy from the standpoint of the cognitive graph. The information demonstrates that the convolution technique and an attention mechanism work well together to increase relationship extraction efficiency. Further evidence that the cultivation of multicultural literacy in English teaching focuses on language ability, learning ability, thinking character, and cultural character in order to quickly improve students’ English multicultural literacy comes from the keywords with high word frequency in English multicultural literacy: “language ability (388, 0.899)”, “learning ability (185, 0.449)”, “thinking character” (114, 0.38), and “cultural character” (105, 0.335).</p>
</abstract>

Using the CNN network model and attention mechanism, this paper completes the construction of the cognitive graph pattern layer based on Protégé programming, crawls the relevant data from the dimensions of language ability, learning ability, thinking, and cultural character of multicultural literacy in English teaching, extracts the entity-relationship of multicultural literacy in English teaching, stores it in the Neo4j corpus, and ultimately completes the construction of the cognitive graph for the cultivation of multicultural literacy in English teaching. Following simulated experiments to evaluate the algorithm’s performance, a case study was carried out to foster English multicultural literacy from the standpoint of the cognitive graph. The information demonstrates that the convolution technique and an attention mechanism work well together to increase relationship extraction efficiency. Further evidence that the cultivation of multicultural literacy in English teaching focuses on language ability, learning ability, thinking character, and cultural character in order to quickly improve students’ English multicultural literacy comes from the keywords with high word frequency in English multicultural literacy: “language ability (388, 0.899)”, “learning ability (185, 0.449)”, “thinking character” (114, 0.38), and “cultural character” (105, 0.335).

A Study on the Cultivation of Multicultural Literacy in College English Teaching Based on the Construction of Cognitive Maps

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Using the CNN network model and attention mechanism, this paper completes the construction of the cognitive graph pattern layer based on Protégé programming,...

A Study on the Cultivation of Multicultural Literacy in College English Teaching Based on the Construction of Cognitive Maps

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 10 apr 2024

Accettato: 28 giu 2024

DOI: https://doi.org/10.2478/amns-2024-2052

Parole chiave
Attention mechanisms, Neo4j, CNN networks, Cognitive mapping, Multicultural literacy

© 2024 Qi Chai, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Study on the Cultivation of Multicultural Literacy in College English Teaching Based on the Construction of Cognitive Maps

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 10 apr 2024

Accettato: 28 giu 2024

DOI: https://doi.org/10.2478/amns-2024-2052

Parole chiaveAttention mechanisms, Neo4j, CNN networks, Cognitive mapping, Multicultural literacy

© 2024 Qi Chai, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Attention mechanisms, Neo4j, CNN networks, Cognitive mapping, Multicultural literacy