Against the backdrop of massive sky survey data, the automated detection, classification, and parameter computation of targets have emerged as critical areas demanding urgent breakthroughs. However, in detection and classification tasks, model accuracy is often constrained by issues such as small target sizes and insufficient feature information. To address this challenge, we innovatively constructs a fully automated astronomical image analysis pipeline that combines point source detection and classification, galaxy morphological classification, and parameter computation, forming an end-to-end solution. This pipeline achieves automated detection and morphological classification of both point sources and extended sources, and it is also able to compute the basic parameters of galaxy targets. The pipeline first accomplishes the detection and localization of target sources using the YOLOv9 model, and then leverages the optimized ResNet-AE model to initially categorize the detected targets into three major classes: stars, quasars, and galaxies. To tackle the problem of small sizes in some galaxy targets, we filtered out samples with larger sizes and distinct contours. Drawing on morphological characteristics, these samples were further classified into six categories via the DenseNet-SE4 model: barred spiral galaxies, cigar galaxies, elliptical galaxies, intermediate galaxies, spiral galaxies, and irregular galaxies. Following this classification, parameter computation was conducted on the targets. Experimental results show that the detection model has achieved better performance than previous studies, with a mean average precision of 85.20% at Intersection over Union values ranging from 0.5 to 0.95. Both classification models also reached an accuracy of over 85% on the test set. Compared with classical CNN networks, these two classification models boast higher precision, and the computation of target parameters has also yielded reliable outcomes. Experiments verify that this pipeline can act as a supplementary tool for astronomical image processing and be applied to data mining and analysis work in sky surveys.