Drug-target prediction method can effectively reduce costs and accelerate research process compared with traditional drug discovery. However， there are various challenges such as low balance of datasets and low precision of prediction in practical applications. Therefore， a drug-target interaction prediction method based on self-adaptive spherical evolution was proposed， namely ASE-KELM （self-Adaptive Spherical Evolution based on Kernel Extreme Learning Machine）. By the method， negative samples with high confidence were selected based on the principle that drugs with similar structures are likely to interact with targets. And to solve the problem that spherical evolution algorithm tends to fall into local optima， the feedback mechanism of historical memory of search factors and Linear Population Size Reduction （LPSR） were used to balance global and local search， which improved the optimization ability of the algorithm. Then the parameters of Kernel Extreme Learning Machine （KELM） were optimized by the self-adaptive spherical evolution algorithm. ASE-KELM was compared with algorithms such as NetLapRLS （Network Laplacian Regularized Least Square） and BLM-NII （Bipartite Local Model with Neighbor-based Interaction profile Inferring） on gold standard based datasets to verify the performance of the algorithms. Experimental results show that ASE-KELM outperforms comparison algorithms in AUC （Area Under the receiver operating Characteristic curve） and AUPR （Area Under the Precision-Recall curve） for the Enzyme （E）， G-Protein-Coupled Receptor （GPCR）， Ion Channel （IC）， and Nuclear Receptor （NR） datasets. And the effectiveness of ASE-KELM in predicting new drug-target pairs was validated on databases such as DrugBank.

As a multi-task meta learning algorithm， Model Agnostic Meta Learning （MAML） can use different models and adapt quickly to different tasks， but it still needs to be improved in terms of training speed and accuracy. The principle of MAML was analyzed from the perspective of Gaussian stochastic process， and a new Model Agnostic Meta Learning algorithm based on Bayesian Weight function （BW-MAML） was proposed， in which the weight was assigned by Bayesian analysis. In the training process of BW-MAML， each sampling task was regarded as following a Gaussian distribution， and the importance of the task was determined according to the probability of the task in the distribution， and then the weight was assigned according to the importance， thus improving the utilization of information in each gradient descent. The small sample image learning experimental results on Omniglot and Mini-ImageNet datasets show that by adding Bayesian weight function， for training effect of BW-MAML after 2500 step with 6 tasks， the accuracy of BW-MAML is at most 1.9 percentage points higher than that of MAML， and the final accuracy is 0.907 percentage points higher than that of MAML on Mini-ImageNet averagely； the accuracy of BW-MAML on Omniglot is also improved by up to 0.199 percentage points averagely.

Aiming at the problems of complex background， insufficient feature extraction ability， small target size， difficult detection， and missed detection in target detection of unmanned aerial vehicles from high-altitude view， an improved target detection algorithm based on YOLOV8n for unmanned aerial vehicles from high-altitude view was proposed. Firstly， the network structure was optimized， and small target perception ability was improved by adding P2 small target detection layer and deleting P5 large target detection layer. Secondly， Receptive Field Attention Convolution （RFAConv） was introduced to improve the Bottleneck of C2f， and the capabilities of feature extraction and fusion were enhanced from spatial dimension. Thirdly， in order to enhance the capabilities of expression and generalization， Dynamic head （Dyhead） module was introduced into Detect detection head. Finally， Normalized Wasserstein Distance （NWD） was used in bounding box similarity measurement to reduce scale sensitivity. The improved YOLOv8n， YOLOv9t and YOLOv10n increase the Average Precision （AP） by 15.6%， 16.7% and 31.0%， respectively， on Visdrone2019 dataset. The detection results on SAR Ship Detection Dataset （SSDD） confirm that the improved algorithm has a strong generalization capability and is more robust. It can be seen that the improved algorithm enhances small target feature extraction and fusion capabilities and has better detection effects in small target detection.