# Research on the construction of a sustainable scientific research capability evaluation model for university teachers based on the T-S fuzzy neural network

## TL;DR

This paper proposes a new model using T-S fuzzy neural networks to evaluate university teachers' sustainable research capabilities with high accuracy.

## Contribution

A novel T-S fuzzy neural network model for evaluating sustainable research capabilities of university teachers with high accuracy.

## Key findings

- The T-S fuzzy neural network model achieved 98.15% accuracy in evaluating research capabilities.
- The model outperformed Naive Bayes and BP neural networks in effectiveness.
- The model is applicable for objectively assessing university teachers' sustainable research capabilities.

## Abstract

This study aims to enhance educational quality and academic standards by proposing a model based on critical research ability indicators to objectively evaluate the sustainable scientific research capabilities of university teachers.

Using T-S fuzzy neural network technology, we developed an evaluation model to measure the sustainability of university teachers’ research capabilities. We collected data from 126 university teachers, using 90 samples for training and 36 for testing, to ascertain the model’s applicability and accuracy.

The T-S fuzzy neural network showcased exceptional learning efficiency and achieved a 98.15% accuracy rate in assessing the sustainable scientific research capabilities of university teachers, outperforming both Naive Bayes and BP neural networks in effectiveness.

The research successfully constructs a T-S fuzzy neural network-based evaluation model for assessing the sustainable scientific research capabilities of university teachers. With high accuracy and broad applicability, this model is an effective tool for objectively evaluating university teachers’ research capabilities, clearly achieving the study’s objective.