Abstract: A hotel demand evaluation method includes setting a plurality of keywords, collecting a plurality of texts according to the plurality of keywords, selecting a plurality of valid texts from the plurality of texts, performing a semantic analysis operation to identify at least one time keyword and at least one location keyword in the plurality of valid texts, generating a classification result according to at least the at least one time keyword and the at least one location keyword, classifying each valid text of the plurality of valid texts into a positive impact group, a no impact group or a negative impact group according to the classification result, and generating a hotel demand score of a specific region according to at least one valid text of the positive impact group and/or at least one valid text of the negative impact group.

Abstract: A method for predicting operational indicators includes performing a training operation according to first data to train a demand model, inputting second data into the demand model to generate predicted demand data, collecting actual demand data, and performing an adjustment operation according to the predicted demand data and the actual demand data to update the demand model.

Abstract: A method for feature extraction and data prediction based on pre-training, includes building a first neural network, inputting first processed data to the first neural network to perform a first training operation to generate a first trained neural network, inputting second processed data to the first trained neural network and fixing a first portion of neurons of the first trained neural network to perform a second training operation to generate a second trained neural network, and inputting third processed data to the second trained neural network to generate a predicted result. A first portion of neurons of the second trained neural network is the same as the first portion of neurons of the first trained neural network.

Abstract: A method for allocating perishable products based on machine learning, includes using a sales estimation model to evaluate estimated sales of a plurality of perishable products in a predetermined period, using a rating model to calculate a predetermined rate of the plurality of perishable products in the predetermined period according to the estimated sales, using an allocation model to adjust an allocation ratio of the plurality of perishable products in a plurality of marketing channels according to the estimated sales and the predetermined rate if a current rate is lower than the predetermined rate, and determining the numbers of perishable products allocated to the plurality of marketing channels according to the allocation ratio.

Abstract: A rate adjustment method includes a rate estimation model generating a plurality of estimated rates according to a plurality of training data, a revenue estimation model generating an estimated revenue according to the plurality of estimated rates, updating the rate estimation model according to the estimated revenue to generate an updated rate estimation model, and inputting a plurality of current data into the updated rate estimation model to update the plurality of estimated rates.

Abstract: A method for retrieving data on a web page includes performing a simulated user operation on a target web page to generate a result web page, retrieving a source code of the result web page, creating a data table according to the source code, and performing a data cleaning operation with the data table to generate cleaned data and store the cleaned data in a database. Each temporary row of the data table is corresponding to a quotation plan.