The traditional age estimation methods based on ranking and regression cannot effectively utilize the evolutionary characteristics of human faces and build correlation between different ranking labels. Moreover， using binary classification methods for age estimation may result in inconsistent ranking issues. To solve above problems， an age estimation method based on integrated ranking matrix encoding and consistency preserving was proposed to fully utilize the correlation between age and ranking value and suppress the problem of inconsistent ranking. A new indicator， the proportion of samples with inconsistent ranking， was proposed to evaluate the problem of inconsistent rankings in the two-class ranking method. First， age categories were converted into a ranking matrix form through a designed coding method. Then， the ResNet34 （Residual Network） feature extraction network was used to extract facial features， which were then learned through the proposed encoding learning module. Finally， the network prediction results were decoded into the predicted age of the image through a ranking decoder based on a metric method. The experimental results show that： the proposed method achieves a Mean Absolute Error （MAE） of 2.18 on MORPH Ⅱ dataset， and has better results on other publicly available datasets compared to methods also based on ranking and ordinal regression， such as OR-CNN （Ordinal Regression with CNN） and CORAL （COnsistent RAnk Logits）； at the same time， the proposed method decreases the proportion of samples with inconsistent ranking， and improves the measurement performance of ranking inconsistency by about 65% compared to the OR-CNN method.

Current research on Application Program Interface （API） learning and code reuse focuses on mining frequent API usage patterns， extracting component information， and recommending personalized API services based on user requirements and target functions. However， as beginners in software development who lack professional knowledge， experience and skills to implement specific use cases， they often need real code use cases as a reference except reading official documents. Most of the existing research about code recommendation is in single fragment mode. The lack of cross function case in case selection is not conducive for beginners to learn to build a complete use scenario or a functional module. At the same time， the semantic description extracted from a single function annotation is not enough for learners to understand the complete function implementation method of the project. To solve the above problems， an API use case recommendation service based on open source community analysis was proposed. Taking the software development back?end framework Spring Boot as an example， a cross function case recommendation assistant learning service was constructed. Then， the feasibility and effectiveness of the proposed API use case recommendation service was verified through questionnaires and expert verification.