The outlier detection algorithm based on autoencoder is easy to over-fit on small- and medium-sized datasets， and the traditional outlier detection algorithm based on ensemble learning does not optimize and select the base detectors， resulting in low detection accuracy. Aiming at the above problems， an Ensemble learning and Autoencoder-based Outlier Detection （EAOD） algorithm was proposed. Firstly， the outlier values and outlier label values of the data objects were obtained by randomly changing the connection structure of the autoencoder generate different base detectors. Secondly， local region around the object was constructed according to the Euclidean distance between the data objects calculated by the nearest neighbor algorithm. Finally， based on the similarity between the outlier values and the outlier label values， the base detectors with strong detection ability in the region were selected and combined together， and the object outlier value after combination was used as the final outlier value judged by EAOD algorithm. In the experiments， compared with the AutoEncoder （AE） algorithm， the proposed algorithm has the Area Under receiver operating characteristic Curve （AUC） and Average Precision （AP） scores increased by 8.08 percentage points and 9.17 percentage points respectively on Cardio dataset； compared with the Feature Bagging （FB） ensemble learning algorithm， the proposed algorithm has the detection time cost reduced by 21.33% on Mnist dataset. Experimental results show that the proposed algorithm has good detection performance and real-time performance under unsupervised learning.

To address the issues of negative transfer and difficulty of information sharing between loosely correlated tasks in multi-task learning model， a cross-layer data sharing based multi-task model was proposed. The proposed model pays attention to fine-grained knowledge sharing， and is able to retain the memory ability of shallow layer shared experts and generalization ability of deep layer specific task experts. Firstly， multi-layer shared experts were unified to obtain public knowledge among complicatedly correlated tasks. Then， the shared information was transferred to specific task experts at different layers for sharing partial public knowledge between the upper and lower layers. Finally， the data sample based gated network was used to select the needed information for different tasks autonomously， thereby alleviating the harmful effects of sample dependence to the model. Compared with the Multi-gate Mixture-Of-Experts （MMOE） model， the proposed model improved the F1-score of two tasks by 7.87 percentage points and 1.19 percentage points respectively on UCI census-income dataset. The proposed model also decreased the Mean Square Error （MSE） value of regression task to 0.004 7 and increased the Area Under Curve （AUC） value of classification task to 0.642 on MovieLens dataset. Experimental results demonstrate that the proposed model is suitable to improve the influence of negative transfer and can learn public information among complicated related tasks more efficiently.