The existing image captioning methods only focus on the grid spatial location features without enough grid feature interaction and full use of image global features. To generate higher-quality image captions， a global image captioning method based on Graph ATtention network （GAT） was proposed. Firstly， a multi-layer Convolutional Neural Network （CNN） was utilized for visual encoding， extracting the grid features and entire image features of the given image and building a grid feature interaction graph. Then， by using GAT， the feature extraction problem was transformed into a node classification problem， including a global node and many local nodes， and the global and local features were able to be fully utilized after updating the optimization. Finally， through the Transformer-based decoding module， the improved visual features were adopted to realize image captioning. Experimental results on the Microsoft COCO dataset demonstrated that the proposed method effectively captured the global and local features of the image， achieving 133.1% in CIDEr （Consensus-based Image Description Evaluation） metric. It can be seen that the proposed image captioning method is effective in improving the accuracy of image captioning， thus allowing processing tasks such as classification， retrieval， and analysis of images by words.

To solve the problem that the direct use of high-dimensional， high-frequency， noise-containing real-world data to perform data processing leads to unreliable estimators， a data uncertainty quantification method based on Generative Adversarial Network （GAN） was proposed. Firstly， the original data distribution was reconstructed by GAN to construct a mapping distribution from the noise space to the space of the original data. Secondly， the samples were extracted by Markov Chain Monte Carlo （MCMC） method to obtain new samples based on the original data distribution. Thirdly， confidence intervals for the uncertainty of the samples were defined based on the specified functions. Finally， the confidence intervals were used to estimate the uncertainty of the original data， and within the data the confidence intervals was selected as the data used by the estimator. Experimental results show that 50% fewer samples are required to train the estimator to reach the upper limit by using the data within the confidence intervals compared to the samples required by using the original data. At the same time， compared to the original data， the data within the confidence intervals requires 30% fewer samples on average to achieve the same test accuracy.