Abstract: Laser powder bed fusion (LPBF) technology, a cutting-edge process in metal additive manufacturing, has been successfully applied in high-end manufacturing sectors like aerospace. However, strong multi-physical field coupling effects frequently lead to dynamic instability in the molten pool, causing widespread porosity defects within fabricated parts and severely impacting forming quality stability. Traditional monitoring methods face limitations such as high cost and deployment difficulties, struggling to meet industrial production demands. To address these challenges, this paper proposes an online monitoring and intelligent internal quality discrimination method based on acoustic emission (AE)-deep learning fusion. An AE sensor-based online monitoring system for the LPBF process was developed. By continously monitoring AE signals throughout the entire process, the mapping relationship between AE signal characteristics and forming quality was revealed, creating a molten pool AE dataset comprising over 80,000 samples. To tackle the difficulty of extracting weak fluctuation features from the molten pool, a frequency domain feature extraction network based on the adaptive Fourier neural operator (AFNO) and a high-dimensional feature mapping classifier based on the Kolmogorov-Arnold network (KAN) were constructed. This approach analyzes molten pool dynamic characteristics through a multi-scale time domain feature fusion mechanism. By precisely mapping high-dimensional features using high-dimensional manifolds, the method achieves enhanced characterization of weak fluctuation features in AE signals and high-precision quality discrimination. Experimental results demonstrate that developed monitoring system effectively captures the dynamic behavior of the molten pool, and the proposed method achieves a quality discrimination accuracy exceeding 97%.