Semantic similarity systems face a fundamental trade-off between domain expertise and multilingual capability, as single embedding spaces cannot preserve both specialized knowledge and cross-linguistic connections. We decompose semantic similarity into three specialist layers—domain-specific, cross- linguistic, and cross-domain—fused with context-adaptive weights.On 783K scientific concepts (6 domains, 8 languages), the approach yields 15% higher Pearson correlation than strong ensembles (r = 0.831 vs 0.748, p < 0.001) at 1.1× computational cost. MTEB evaluation shows consistent 12% gains across 14 tasks. Our theoretical analysis provides mathematical proofs of superiority with O(d) complexity bounds and convergence guarantees.Production deployment on AQEA Universal Platform processes 783K+ concepts with 16.8ms latency and 99.97% uptime. Multi-Layer Network Theory establishes the first systematic solution to the semantic compression problem, enabling AI systems that maintain specialized expertise while preserving global multilingual accessibility. The framework’s theoretical rigor, comprehensive validation, and production success position it for immediate adoption across scientific, educational, and commercial applications.