Large Language Models have shown strong performance in Machine Translation, yet they often suffer from paraphrasing errors, omissions, or hallucinations when the input contains translation-specific elements (e.g., URLs, slang, and idioms) that require strict preservation or controlled transformation, undermining the reliability of critical details.We propose CEMT, a Controllable Element-Oriented Machine Translation framework inspired by the analysis–strategy–generation paradigm in human translation. CEMT first employs an Element Detection Module to identify translation-specific elements, and then introduces a Translation Module that decomposes the translation process into linguistically grounded analysis, strategy formulation, and final generation, thereby guiding the reliable translation of these elements. We further introduce a CoT Judge model during training that provides step-wise supervision over the accuracy and consistency of the translation process.On the WMT23/24 Chinese–English benchmarks, CEMT improves performance over existing Machine Translation models while significantly reducing element-level constraint violations.