This paper compares Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), on a wide range of natural language processing (NLP) tasks. The study aims to provide guidance for selecting the most suitable architecture for different NLP tasks. The results show that CNNs and RNNs provide complementary information for text classification tasks. Which architecture performs better depends on how important it is to semantically understand the whole sequence. Learning rate changes performance relatively smoothly, while changes to hidden size and batch size result in large fluctuations. The study evaluates CNNs, GRUs, and LSTMs on tasks such as sentiment classification, relation classification, textual entailment, answer selection, question-relation matching, path query answering, and part-of-speech tagging. The results indicate that CNNs perform well on tasks where local features are important, while RNNs are better suited for tasks requiring understanding of long-range dependencies. For example, CNNs outperform RNNs in tasks like answer selection where key phrases are important, while RNNs perform better in tasks like textual entailment where understanding the whole sentence is crucial. The study also finds that the performance of CNNs and RNNs is sensitive to hyperparameters such as learning rate, hidden size, and batch size. While CNNs are generally less sensitive to these parameters, RNNs show more variability. The results suggest that optimizing these hyperparameters is crucial for achieving good performance with both CNNs and RNNs. Overall, the study concludes that there is no single best architecture for all NLP tasks, and the choice of architecture depends on the specific requirements of the task.This paper compares Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), on a wide range of natural language processing (NLP) tasks. The study aims to provide guidance for selecting the most suitable architecture for different NLP tasks. The results show that CNNs and RNNs provide complementary information for text classification tasks. Which architecture performs better depends on how important it is to semantically understand the whole sequence. Learning rate changes performance relatively smoothly, while changes to hidden size and batch size result in large fluctuations. The study evaluates CNNs, GRUs, and LSTMs on tasks such as sentiment classification, relation classification, textual entailment, answer selection, question-relation matching, path query answering, and part-of-speech tagging. The results indicate that CNNs perform well on tasks where local features are important, while RNNs are better suited for tasks requiring understanding of long-range dependencies. For example, CNNs outperform RNNs in tasks like answer selection where key phrases are important, while RNNs perform better in tasks like textual entailment where understanding the whole sentence is crucial. The study also finds that the performance of CNNs and RNNs is sensitive to hyperparameters such as learning rate, hidden size, and batch size. While CNNs are generally less sensitive to these parameters, RNNs show more variability. The results suggest that optimizing these hyperparameters is crucial for achieving good performance with both CNNs and RNNs. Overall, the study concludes that there is no single best architecture for all NLP tasks, and the choice of architecture depends on the specific requirements of the task.