Partial Label Regression （PLR） complements the current situation that Partial Label Learning （PLL） only focuses on classification tasks. To address the problem that the existing PLR algorithms ignore characteristic differences between instance features， a Partial Label Regression algorithm integrating Feature Attention and Residual Connection （PLR-FARC） was proposed. Firstly， labels of real datasets were expanded into a set of real-value candidate labels by label enhancement technique. Secondly， the attention mechanism was employed to generate contribution of individual features to labels automatically. Thirdly， the residual connection was introduced to reduce information loss and maintain feature integrity during feature transmission. Finally， prediction loss was calculated based on IDent （IDentification method） and PIDent （Progressive IDentification method）， respectively. Experimental results on Abalone， Airfoil， Concrete， Cpu-act， Housing and Power-plant datasets show that compared to IDent and PIDent， PLR-FARC has the Mean Absolute Error （MAE） reduced by 2.15%， 38.38%， 8.86%， 4.19%， 15.71% and 15.55%， averagely and respectively， and the Mean Squared Error （MSE） reduced by 9.35%， 71.32%， 23.10%， 20.17%， 27.22% and 9.46%， averagely and respectively. It can be seen that the proposed algorithm is feasible and effective.