Reading right books contributes to children's imagination and brain
development, enhances their language and emotional comprehension abilities, and
strengthens their relationships with others. Building upon the critical role of
reading books in individual development, this paper aims to develop an
algorithm that determines the suitability of books for middle school students
by analyzing their structural and semantic features. Using methods described,
an algorithm will be created that can be utilized by institutions and
individuals responsible for children's education, such as the Ministry of
National Education officials and schools. This algorithm will facilitate the
selection of books to be taught at the middle school level. With the algorithm,
the book selection process for the middle school curriculum can be expedited,
and it will serve as a preliminary reference source for those who evaluate
books by reading them. In this paper, the Python programming language was
employed, utilizing natural language processing methods. Additionally, an
artificial neural network (ANN) was trained using the data which had been
preprocessed to construct an original dataset. To train this network, suitable
books for middle school students were provided by the MEB, Oxford and Cambridge
and with content assessed based on the "R" criterion, and inappropriate books
for middle school students in terms of content were included. This trained
neural network achieved a 90.06% consistency rate in determining the
appropriateness of the test-provided books. Considering the obtained findings,
it can be concluded that the developed software has achieved the desired
objective.

本文旨在通过分析结构和语义特征，开发一种算法来确定适合中学生的书籍，并利用 Python 编程语言和人工神经网络对算法进行训练，以选择中学课程的书籍。通过对数据的预处理和构建原始数据集，训练的神经网络在确定测试提供的书籍的适宜程度方面达到了 90.06％的一致性率，从而实现了预期目标。

使用人工神经网络识别适合中学生的图书

Identification of Books That are Suitable for Middle School Students  Using Artificial Neural Networks

Books have the power to make us feel happiness, sadness, pain, surprise, or
sorrow. An author's dexterity in the use of these emotions captivates readers
and makes it difficult for them to put the book down. In this paper, we model
the flow of emotions over a book using recurrent neural networks and quantify
its usefulness in predicting success in books. We obtained the best weighted
F1-score of 69% for predicting books' success in a multitask setting
(simultaneously predicting success and genre of books).

本文利用循环神经网络模拟书籍中情感的变化，并量化其预测书籍成功的有效性。在同时预测书籍成功和类型的多任务设置中，取得了 69% 的最佳加权 F1 得分。