With the rapid development of Location-Based Services (LBSs), research on Indoor Positioning Systems (IPS) has gained extensive attention. Although existing channel state information (CSI)-based positioning algorithms have achieved good positioning accuracy, they still face significant challenges in practical applications due to their high computational complexity and cost. To address this, this paper proposes an indoor positioning method based on the frequency fading characteristics of broadband signals, named the ACCS algorithm. This method introduces the innovative use of the autocorrelation spectrum of broadband signals to transform the indoor positioning problem into an image classification task. After that, to achieve lightweight model design, a convolutional autoencoder (CAE), which is an unsupervised learning model based on convolutional neural networks, is employed to reduce the dimension and compress the input images. Finally, a deep residual network combined with a CS-Attention module, which is composed of a channel feature extraction module and a spatial feature extraction module, is used to efficiently capture image features for more accurate position estimation. The performance of the proposed method was tested using data collected in a conference room. Experimental results show that the autocorrelation processing and attention module in the proposed algorithm significantly improve positioning accuracy, achieving a positioning error of 0.245 m in complex indoor environments. Additionally, comparisons with common positioning algorithms (AOA, W-KNN) demonstrate that our method exhibits significant advantages in positioning accuracy and robustness.