Research on the Evaluation and Improvement Algorithm of Smart Elderly Care System Based on Big Data Technology Based on LSTM and AdaBoost

In the evaluation of smart elderly care system, an innovative and accurate evaluation model can be built by combining Long short-term memory network (LSTM) and adaptive enhancement integrated algorithm AdaBoost. LSTM is a recursive neural network structure, which is especially suitable for processing time series data, and can effectively capture the data patterns that change over time in the process of elderly care service, such as the health status of the elderly, the changing trend of service demand, and the response effect of the system.In this scheme, LSTM network is first used to capture and learn the long-term dependence relationship and potential rules in the operation of the elderly care system, and through in-depth analysis of historical data, fine prediction and evaluation of the quality and effect of the elderly care service is formed. However, a single model may be limited by the risk of overfitting or underfitting, in which case the introduction of AdaBoost algorithm can significantly improve the robustness and generalization ability of the model. AdaBoost constructs and combines weak classifiers iteratively to create a strong classifier, which means tradeoff optimization among different evaluation indicators and improvement of overall evaluation performance for elderly care service evaluation. Taking LSTM as the basic learner within the framework of AdaBoost, the weight of LSTM can be adjusted according to the performance of LSTM in each iteration, so that the final integrated model can focus on those difficult cases and features with high discriminating power, so as to achieve more accurate and comprehensive evaluation results of pension system. In short, the combination of LSTM and AdaBoost in elderly care system evaluation not only makes full use of LSTM's powerful modeling ability for time series data, but also gives full play to AdaBoost's advantages in integrated learning, which helps to extract key evaluation indicators from diverse and complex elderly care service data thus providing more scientific and reliable support for optimizing the elderly care service system.