Current deep learning-based methods for crop disease recognition rely on specific image datasets of crop diseases for image representation learning， and do not consider the importance of text features in assisting image feature learning. To enhance feature extraction and disease recognition capabilities of the model for crop disease images more effectively， a Crop Disease Recognition method through multi-modal data fusion based on Contrastive Language-Image Pre-training （CDR-CLIP） was proposed. Firstly， high-quality disease recognition image-text pair datasets were constructed to enhance image feature representation through textual information. Then， a multi-modal fusion strategy was applied to integrate text and image features effectively， which strengthened the model capability of distinguishing diseases. Finally， specialized pre-training and fine-tuning strategies were designed to optimize the model’s performance in specific crop disease recognition tasks. Experimental results demonstrate that CDR-CLIP achieves the disease recognition accuracies of 99.31% and 87.66% with F1 values of 99.04% and 87.56%， respectively， on PlantVillage and AI Challenger 2018 crop disease datasets. On PlantDoc dataset， CDR-CLIP achieves the mean Average Precision mAP@0.5 of 51.10%， showing the strong performance advantage of CDR-CLIP.

The recognition of spam is one of the main tasks in natural language processing. The traditional methods are based on text features or word frequency， which recognition accuracies mainly depend on the presence or absence of specific keywords. When there are no keywords or errors in recognizing keywords in the spam， the traditional methods have poor recognition performance. Neural network-based methods were proposed. Recognition training and testing were conducted on complex spam. The spams that cannot be recognized by traditional methods were collected and the same amount of normal information was randomly selected from spam messages， advertisement and spam email datasets to form three new datasets without duplicate data. Three models were proposed based on convolutional neural network and recurrent neural network and tested on three new datasets for spam recognition. The experimental results show that the neural network-based models learned better semantic features from the text and achieved the accuracies of more than 98% on all three datasets， which are significantly higher than those of the traditional methods， such as Naive Bayes （NB）， Random Forest （RF） and Support Vector Machine （SVM）. The experimental results also show that different neural networks are suitable for text classification with different lengths. The models composed of recurrent neural networks are good at recognizing text with sentence length， the models composed of convolutional neural networks are good at recognizing text with paragraph length， and the models composed of both neural networks are good at recognizing text with chapter length.