As a key module of the question answering system, question classification is also a key factor that restricts the retrieval efficiency of the question answering system. Aiming at the problems of complicated semantic information and large differences of user questions in agricultural question answering system, in order to meet the needs of users to quickly and accurately obtain classification results of common crop disease questions, the question classification model of common crop disease question answering system based on Bidirectional Encoder Representations from Transformers (BERT) was constructed. Firstly, the question dataset was preprocessed. Then, Bidirectional-Long Short Term Memory (Bi-LSTM) self-attention network classification model, Transformer classification model and BERT-based fine-tuning classification model were constructed respectively, and the three models were used to extract information of questions and train question classification model. Finally, the BERT-based fine-tuning classification model was tested and the impact of dataset size on classification results was explored. The experimental results show that, the BERT-based fine-tuning common crop disease question classification model has the classification accuracy, precision, recall, weighted harmonic mean of accuracy and recall higher than those of the Bi-LSTM self-attention network classification model and the Transformer classification model by 2-5 percentage points respectively. On Common Crop Disease Question Dataset (CCDQD), it can obtain the highest accuracy of 92.46%, precision of 92.59%, recall of 91.26%, and weighted harmonic mean of accuracy and recall of 91.92%. The BERT-based fine-tuning classification model has advantages of simple structure, few parameters and fast speed, and can efficiently classify common crop disease questions accurately. So, it can be used as the question classification model for the common crop disease question answering system.

To solve problems such as easily getting trapped in local optimum and slow convergence rate in BareBones Particle Swarm Optimization (BBPSO) algorithm, a dynamic Multi-Subgroup collaboration Barebones Particle Swarm Optimization based on Kernel Fuzzy Clustering (KFC-MSBPSO) was proposed. Based on the standard BBPSO algorithm, firstly, kernel fuzzy clustering method was used to divide the main group into several subgroups, and the subgroups optimized collaboratively to improve the searching efficiency. Then, nonlinear dynamic mutation factor was introduced to control subgroup mutation probabilities according to the number of particles and convergence conditions, the main group was reconstructed by means of particle mutation and the exploration ability was improved. The main group particle absorption strategy and subgroup merge strategy were proposed to strengthen the information exchange between main group and subgroups and enhanced the stability of the algorithm. Finally, the subgroup reconstruction strategy was used to adjust the iterations of subgroup reconstruction by combining the optimal solutions. The results of experiments on six benchmark functions, such as Sphere, show that the accuracy of KFC-MSBPSO algorithm has improved by at least 11.1% compared with classical BBPSO algorithm, Opposition-Based Barebones Particle Swarm Optimization (OBBPSO) algorithm and other improved algorithms. The best mean value in high dimensional space accounts for 83.33% and has a faster convergence rate. This indicates that KFC-MSBPSO algorithm has good search performance and robustness, which can be applied to the optimization of high dimensional complex functions.