  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.
