With the advantages of being scalable, labeled-sample free, and structurally simple, autoencoder (AE) is suitable for hyperspectral target detection (HTD) with discriminative attributes extraction ability. However, there are some bottlenecks in current AE-based approaches, like network complexity, parameter determination, and dictionary reliability problems. In this article, a novel target-driven AE (TAE) with a constrained latent code layer and improved loss function is proposed. To achieve effective noise and interference annihilation, a simple AE-based network is designed with the adaptively determined number of hidden neurons by estimating the ranks of the background (BKG) and the target component. In addition, to untangle the dependence on the BKG dictionary, a novel strategy is imposed on the traditional Kullback–Leibler (KL) divergence, called the truncated KL divergence (TKL), is designed and imposed by the loss function of TAE, guiding the propensity for network reconstruction of the targets over BKG with probabilities rather than dictionaries. Furthermore, unlike AE-based methods with forward strategy, a novel target-driven iterative AE (ITAE) framework is developed to further exploit the potential of prior and posterior knowledge. The iterative system repeatedly trains the same TAE and adds the weighted constrained energy minimization (CEM) detection map back to the current data matrix for the next iteration. ITAE provides a general classic-deep learning collaborative framework that can train any AE with a traditional detector through an iterative process to improve the diversity between the targets and the BKG. To validate the effectiveness of ITAE, six other state-of-the-art HTD methods are chosen for comparison under six datasets of various scenarios. Based on the 3-D receiver operating characteristic (ROC) curve-derived evaluation metric, we conducted experiments for parameter analysis, detection performance comparison, noisy study, and ablation study...