Hyperspectral target detection based on deep learning faces challenges such as insufficient quality of samples, intricate network structures, and laborious parameter adjustment. In this paper, we propose a deep learning method with data augmentation and automatic hyperparameter optimization. To tackle the issue of insufficient quality of samples, we introduce a sample augmentation strategy. The strategy utilizes endmember extraction and clustering techniques to directly acquire a large number of background pixels from hyperspectral images. By pairing these with a small number of known target pixels using a phase-reducing pixel pairing approach, we obtain a large number of labeled pure sample pairs, thereby accomplishing data augmentation. In addition, distinct from most complex deep networks, we designed a lightweight Convolutional Neural Network (CNN) comprised of 12 convolutional layers. This network is specifically engineered to efficiently and rapidly learn the mapping between input sample pairs and their corresponding labels. By incorporating the particle swarm optimization algorithm, this network possesses the capability to automatically optimize hyperparameters, overcoming the shortcomings of laborious parameter adjustment. This enables the network to automatically adjust hyperparameters based on samples from different hyperspectral images, thereby generating optimal results. For a test pixel, the input to the trained network is the spectral difference between the central pixel and its adjacent pixels. When a test pixel belongs to the target, the output score is closely align with the target label. Experimental results on five hyperspectral datasets demonstrate that our method significantly outperforms existing techniques.