To evaluate the effectiveness of intelligent sensory technologies in monitoring the dynamic changes in fruit quality and flavor compounds of Pyrus ussuriensis 'Hongxiangsu' during storage, five sampling time points were set, including cold storage for 0, 60 and 120 days, as well as 3 and 6 days of ambient shelf life following 120 days of cold storage. The flavor difference and other quality indicators of Pyrus ussuriensis 'Hongxiangsu' under 1-methylcyclopropene (1-MCP) treatment during storage were assessed using electronic nose (E-nose) and electronic tongue (E-tongue) analysis combined with physicochemical measurements and gas chromatography-mass spectrometry (GC-MS). The results showed that 1-MCP treatment delayed the decline in the absorbance difference value (I_AD), sucrose content, total organic acids content, malic acid content, and most sensory attributes (except aroma), while suppressing increased in peel a^* value, as well as glucose and fructose contents in fruit flesh and sensory aroma intensity. No significant effects of 1-MCP were observed on fruit firmness, soluble solids content (SSC), titratable acidity (TA), peel L^* value, peel b^* value, fructose content, sorbitol content and glucose content. A total of 51 volatile compounds were detected, among which 1-MCP treatment decreased the relative abundance of ester but increased that of aldehydes, delaying the aroma changes during fruit ripening. Notably, the E-nose sensor array effectively distinguished the aroma characteristics of 'Hongxiangsu' pear during storage, with the W5S sensor closely correlated with sensory aroma. The sourness signal of the E-tongue showed positive correlations with texture, acidity, astringency, as well as pulp firmness, total organic acids, and aldehydes level, but negative correlations with pulp sweetness, L^* value, a^* value, b^* value, esters content, and alkanes level. Overall, this study demonstrates that E-nose and E-tongue technologies can partially reflect traditional sensory evaluation and link pear flavor components, thereby providing a reliable theoretical basis for intelligent postharvest quality monitoring of fruit.