In my dataset all the features are in Categorical. How can i convert it into numbers? I am not able to apply OHE because tags feature has around 100 words in it so how can solve this problem?
Here is my data
    book={'Title':['Geophysical deep learning','Deep learning for algorithm portfolios','Polymer electrolyte and polymer'],
'tags':['method comprising:\nselecting type geophysical data;\nselecting type algorithm;\ngenerating synthetic geophysical data selected type geophysical data based least part algorithm;\ntraining deep learning classifier framework based least part synthetic geophysical data generate trained deep learning classifier framework;\nreceiving acquired geophysical data geologic environment;\nimplementing trained deep learning classifier framework generate interpretation results acquired geophysical data; and\noutputting interpretation results, wherein interpretation results characterize structural','features indicative hydrocarbons geologic environment.  A method comprising:\nselecting a type of geophysical data;\nselecting a type of algorithm;\ngenerating synthetic geophysical data of the selected type of geophysical data based at least in part on the algorithm;\ntraining a deep learning classifier framework based at least in part on the synthetic geophysical data to generate a trained deep learning classifier framework;\nreceiving acquired geophysical data for a geologic environment;\nimplementing the trained deep learning classifier framework to generate interpretation results for the acquired geophysical data; and\noutputting the interpretation results','method suppressing non-specific cross-hybridization repetitive sequences present nucleic acid probes homologous repetitive sequences target genomic nucleic acid, said method comprising steps of:\nidentifying repetitive sequences representative genomic region;\nsynthesizing suppressive nucleic acid derived said identified repetitive sequences, said suppressive nucleic acid substantially comprising said identified repetitive sequences substantially'],
'Target':['Deep Learning','Deep Learning','Polymer']}

pd.DataFrame(book)

In this we have to predict the Target feature on the basis of Book and Tags. so how can i solve this problem? I can't use OHE because in tags feature i have 100 of different words.This is a demo dataset my original dataset has an shape of (200,3)

It looks like you rather want to encode the complete set of words, not each word individually.  In this case a bag-of-words model might be appropriate.  Essentially a dictionary where you count the occurrences of each word in a data point.
If you want to have fixed size vectors as features, you can use the same principle, but instead map only some $N$ most frequent words (determined on the training data) to the entries of a vector (possibly applying the hash trick).
