Six-Class BPT Galaxy Classification for Survey-Scale AGN Candidate Prioritization: Deep Tabular Model and Informative Missingness Signals

The Baldwin--Phillips--Terlevich (BPT) diagram is widely used to classify galaxies into star-forming systems, composite galaxies, and active galactic nuclei (AGNs), but its survey-scale application is limited by the requirement for high signal-to-noise emission-line measurements. We test whether machine-learning models can reproduce six-class BPT labels while using measured quantities, derived line ratios, and potentially informative missing-data patterns as inputs. We analyze 1.47 million galaxies with a 27-dimensional feature set that combines raw survey measurements, derived quantities, and missingness indicators. Five deep tabular architectures are benchmarked against gradient-boosted trees and classical machine-learning baselines, and the resulting probabilities are evaluated through hard-classification metrics, precision--recall curves, top-$k$ retrieval, ablation tests, and feature-interpretation diagnostics. {CNN--Transformer gives the strongest overall classification performance (accuracy = 0.8266), while boosted trees remain highly competitive for this low-dimensional tabular problem. In the binary star-forming-versus-AGN comparison, CNN--Transformer achieves a Class~1 versus Class~4 ROC AUC of 0.9998. Missingness indicators provide substantial predictive information, especially the OH\_P50N\_missing feature. Feature interpretation further shows that $\log([\mathrm{Ne\,III}]/[\mathrm{O\,II}])$, combined with stellar mass or specific star-formation rate, separates star-forming galaxies from AGN hosts. The models are most useful as AGN candidate-ranking tools that complement, rather than replace, traditional BPT diagnostics. High-ranked samples can reach high purity, while broader candidate lists recover most AGNs, but transferability to other surveys requires further validation.