How to use text classification where the training source are txt files in categorized folders?
Assuming that your folders are your classes, you can match any document with the correspondent tag.
Then, for every document:
1.- Normalize the text, i.e. remove stop words (unless they make sense), stem and / or lemmatize it (unless it doesn't makes sense).
2.- Vectorize the documents, you can choose TFIDF, BOW, word embeddings etc
3.- Depending on your documents train with an MLP (in case of BOW) or an LSTM in case of words embeddings.

When you have a new document, you need to repeat the procedure using the vocabulary you created for the training set.

I had a similar use case and was enough to use BOW with a multi layer perceptron, the accuracy was above 95%, but, documents were different for each category and I removed most frequent words because there where to common.

Another solution is to perform a topic modeling on documents binding those topics to the category and then training a simple classifier (an MLP or SVM will work)