Combining methods from artificial intelligence (AI) with lattice theory and partially ordered sets (posets) is shown in this study as a new way to better mine huge amounts of data. It is easier to find patterns and show data with this method because it uses the natural hierarchy and algebraic features of lattice and poset structures. Models like deep learning and reinforcement learning are used by the AI system, which is more complex. When vectorial convergence is possible, vector measure spaces and orthogonal sequences are used most of the time to work with complicated relationship data. Tests have shown that the approach is reliable, works with a wide range of datasets, and can be made bigger.