Text classification using deep learning with removing name, place address etc
Normally if you have enough training data this shouldn't be an issue, because the model will realize that these named entities are not relevant for classifying the text.
Anyway if you want to remove them you would need to first detect them with Named Entity Recognition, there are many implementations. Then you can simply remove them.
Using DL classification as a first option is questionable imho:

it usually requires larger training data
it takes much longer to train than traditional models
it's harder to understand what happens when things go wrong

I'm old school so I'd recommend starting with simple methods, for instance TFIDF vectors with Naive Bayes or Decision Trees.