Recursive Neural Networks (RNNs) are a powerful tool for Natural Language Processing (NLP) that can easily learn and represent recursive structures in sentences. Artificial Neural Networks (ANNs) have been widely used in various NLP tasks, such as language modeling, text classification, and sentiment analysis. With the development of deep learning, RNNs have emerged as a powerful tool to represent structural and syntactic information of sentences. RNNs have attracted a lot of attention due to their ability to learn complex language structures and representation. Consequently, RNNs have achieved great performance in a variety of NLP applications, such as text summarization, machine translation, and question answering. In this paper, we discuss the importance of RNNs in NLP and present different smart performance optimization strategies for RNNs. Firstly, we consider the use of pre-trained language models such as GloVe and word2vec to enhance the representation of words and improve the overall accuracy. Secondly, we implement a variety of regularization techniques, such as dropout and batch normalization, to regularize the learning process of RNNs. Finally, we discuss the approaches used to reduce the complexity and overfitting of RNNs. We compare the performance of different RNNs on the Penn Treebank dataset with different optimization strategies. Experimental results show that our proposed approaches can significantly improve the performance of RNNs and provide a more effective solution for NLP tasks.